BibTeX for papers by David Kotz; for complete/updated list see https://www.cs.dartmouth.edu/~kotz/research/papers.html @PhdThesis{hardin:thesis, author = {Taylor Hardin}, title = {{Information Provenance for Mobile Health Data}}, school = {Dartmouth Computer Science}, year = 2022, month = {May}, copyright = {the author}, address = {Hanover, NH}, URL = {https://www.cs.dartmouth.edu/~kotz/research/hardin-thesis/index.html}, abstract = { Mobile health (mHealth) apps and devices are increasingly popular for health research, clinical treatment and personal wellness, as they offer the ability to continuously monitor aspects of individuals' health as they go about their everyday activities. Many believe that combining the data produced by these mHealth apps and devices may give healthcare-related service providers and researchers a more holistic view of an individual's health, increase the quality of service, and reduce operating costs. For such mHealth data to be considered useful though, data consumers need to be assured that the authenticity and the integrity of the data has remained intact --- especially for data that may have been created through a series of aggregations and transformations on many input data sets. In other words, \emph{information provenance} should be one of the main focuses for any system that wishes to facilitate the sharing of sensitive mHealth data. Creating such a trusted and secure data sharing ecosystem for mHealth apps and devices is difficult, however, as they are implemented with different technologies and managed by different organizations. Furthermore, many mHealth devices use ultra-low-power micro-controllers, which lack the kinds of sophisticated Memory Management Units (MMUs) required to sufficiently isolate sensitive application code and data. \par In this thesis, we present an end-to-end solution for providing information provenance for mHealth data, which begins by securing mHealth data at its source: the mHealth device. To this end, we devise a memory-isolation method that combines compiler-inserted code and Memory Protection Unit (MPU) hardware to protect application code and data on ultra-low-power micro-controllers. Then we address the security of mHealth data outside of the source (e.g., data that has been uploaded to smartphone or remote-server) with our health-data system, Amanuensis, which uses Blockchain and Trusted Execution Environment (TEE) technologies to provide confidential, yet verifiable, data storage and computation for mHealth data. Finally, we look at identity privacy and data freshness issues introduced by the use of blockchain and TEEs. Namely, we present a privacy-preserving solution for blockchain transactions, and a freshness solution for data access-control lists retrieved from the blockchain. }, } @Article{boateng:stepcount, author = {George Boateng and Curtis L. Petersen and David Kotz and Karen L. Fortuna and Rebecca Masutani and John A. Batsis}, title = {{A Smartwatch Step-Counting App for Older Adults: Development and Evaluation Study}}, journal = {JMIR Aging}, year = 2022, month = {August}, day = 10, volume = 5, number = 3, articleno = {e33845}, numpages = 11, publisher = {JMIR Publications}, copyright = {the authors}, DOI = {10.2196/33845}, URL = {https://www.cs.dartmouth.edu/~kotz/research/boateng-stepcount/index.html}, abstract = {\emph{Background:} Older adults who engage in physical activity can reduce their risk of mobility impairment and disability. Short amounts of walking can improve quality of life, physical function, and cardiovascular health. Various programs have been implemented to encourage older adults to engage in physical activity, but sustaining their motivation continues to be a challenge. Ubiquitous devices, such as mobile phones and smartwatches, coupled with machine-learning algorithms, can potentially encourage older adults to be more physically active. Current algorithms that are deployed in consumer devices (eg, Fitbit) are proprietary, often are not tailored to the movements of older adults, and have been shown to be inaccurate in clinical settings. Step-counting algorithms have been developed for smartwatches, but only using data from younger adults and, often, were only validated in controlled laboratory settings. \par \emph{Objective:} We sought to develop and validate a smartwatch step-counting app for older adults and evaluate the algorithm in free-living settings over a long period of time. \par \emph{Methods:} We developed and evaluated a step-counting app for older adults on an open-source wrist-worn device (Amulet). The app includes algorithms to infer the level of physical activity and to count steps. We validated the step-counting algorithm in the lab (counting steps from a video recording, n{$=$}20) and in free-living conditions---one 2-day field study (n{$=$}6) and two 12-week field studies (using the Fitbit as ground truth, n{$=$}16). During app system development, we evaluated 4 walking patterns: normal, fast, up and down a staircase, and intermittent speed. For the field studies, we evaluated 5 different cut-off values for the algorithm, using correlation and error rate as the evaluation metrics. \par \emph{Results:} The step-counting algorithm performed well. In the lab study, for normal walking (R2{$=$}0.5), there was a stronger correlation between the Amulet steps and the video-validated steps; for all activities, the Amulet's count was on average 3.2 (2.1\%) steps lower (SD 25.9) than the video-validated count. For the 2-day field study, the best parameter settings led to an association between Amulet and Fitbit (R2{$=$}0.989) and 3.1\% (SD 25.1) steps lower than Fitbit, respectively. For the 12-week field study, the best parameter setting led to an R2 value of 0.669. \par \emph{Conclusions:} Our findings demonstrate the importance of an iterative process in algorithm development before field-based deployment. This work highlights various challenges and insights involved in developing and validating monitoring systems in real-world settings. Nonetheless, our step-counting app for older adults had good performance relative to the ground truth (a commercial Fitbit step counter). Our app could potentially be used to help improve physical activity among older adults.}, } @Article{batsis:rural, author = {John A. Batsis and Curtis L. Petersen and Matthew M. Clark and Summer B. Cook and David Kotz and Tyler L. Gooding and Meredith N. Roderka and Rima I. Al-Nimr and Dawna Pidgeon and Ann Haedrich and K.C. Wright and Christina Aquila and Todd A. Mackenzie}, title = {{Feasibility and acceptability of a technology-based, rural weight management intervention in older adults with obesity}}, journal = {BMC Geriatrics}, year = 2021, month = {January}, volume = 21, articleno = 44, numpages = 13, publisher = {BMC}, copyright = {the authors}, DOI = {10.1186/s12877-020-01978-x}, PMID = 33435877, URL = {https://www.cs.dartmouth.edu/~kotz/research/batsis-rural/index.html}, abstract = {\emph{Background:} Older adults with obesity residing in rural areas have reduced access to weight management programs. We determined the feasibility, acceptability and preliminary outcomes of an integrated technology-based health promotion intervention in rural-living, older adults using remote monitoring and synchronous video-based technology. \par \emph{Methods:} A 6-month, non-randomized, non-blinded, single-arm study was conducted from October 2018 to May 2020 at a community-based aging center of adults aged {$\geq$}65 years with a body mass index (BMI) {$\geq$}30 kg/m2. Weekly dietitian visits focusing on behavior therapy and caloric restriction and twice-weekly physical therapist-led group strength, flexibility and balance training classes were delivered using video-conferencing to participants in their homes. Participants used a Fitbit Alta HR for remote monitoring with data feedback provided by the interventionists. An aerobic activity prescription was provided and monitored. \par \emph{Results:} Mean age was 72.9{$\pm$}3.9 years (82\% female). Baseline anthropometric measures of weight, BMI, and waist circumference were 97.8{$\pm$}16.3 kg, 36.5{$\pm$}5.2 kg/m2, and 115.5{$\pm$}13.0 cm, respectively. A total of 142 participants were screened (n{$=$}27 ineligible), and 53 consented. There were nine dropouts (17\%). Overall satisfaction with the trial (4.7+0.6, scale: 1 (low) to 5 (high)) and with Fitbit (4.2+0.9) were high. Fitbit was worn an average of 81.7{$\pm$}19.3\% of intervention days. In completers, mean weight loss was 4.6{$\pm$}3.5 kg or 4.7{$\pm$}3.5\% (p{$<$}0.001). Physical function measures of 30-s sit-to-stand repetitions increased from 13.5{$\pm$}5.7 to 16.7{$\pm$}5.9 (p{$<$}0.001), 6-min walk improved by 42.0{$\pm$}77.3 m (p{$=$}0.005) but no differences were observed in gait speed or grip strength. Subjective measures of late-life function improved (3.4{$\pm$}4.7 points, p{$<$}0.001). \par \emph{Conclusions:} A technology-based obesity intervention is feasible and acceptable to older adults with obesity and may lead to weight loss and improved physical function.}, } @Article{batsis:weight-loss, author = {John A. Batsis and Curtis L. Petersen and Matthew M. Clark and Summer B. Cook and Francisco Lopez-Jimenez and Rima I. Al-Nimr and Dawna Pidgeon and David Kotz and Todd A. Mackenzie and Steven J. Bartels}, title = {{A Weight-Loss Intervention Augmented by a Wearable Device in Rural Older Adults with Obesity: A Feasibility Study}}, journal = {Journals of Gerontology - Series A: Biological Sciences and Medical Sciences}, year = 2021, month = {January}, volume = 76, number = 1, pages = {95--100}, publisher = {Oxford Academic}, copyright = {the authors}, DOI = {10.1093/gerona/glaa115}, URL = {https://www.cs.dartmouth.edu/~kotz/research/batsis-weight-loss/index.html}, note = {First published 8 May 2020}, abstract = { \emph{Background:} Older persons with obesity aged 65+ residing in rural areas have reduced access to weight management programs due to geographic isolation. The ability to integrate technology into health promotion interventions shows a potential to reach this underserved population. \par \emph{Methods:} A 12-week pilot in 28 older rural adults with obesity (body mass index [BMI] {$\geq$} 30 kg/m2) was conducted at a community aging center. The intervention consisted of individualized, weekly dietitian visits focusing on behavior therapy and caloric restriction with twice weekly physical therapist-led group strengthening training classes in a community-based aging center. All participants were provided a Fitbit Flex 2. An aerobic activity prescription outside the strength training classes was provided. \par \emph{Results:} Mean age was 72.9 {$\pm$} 5.3 years (82\% female). Baseline BMI was 37.1 kg/m2, and waist circumference was 120.0 {$\pm$} 33.0 cm. Mean weight loss (pre/post) was 4.6 {$\pm$} 3.2 kg (4.9 {$\pm$} 3.4\%; p {$<$} .001). Of the 40 eligible participants, 33 (75\%) enrolled, and the completion rate was high (84.8\%). Objective measures of physical function improved at follow-up: 6-minute walk test improved: 35.7 {$\pm$} 41.2 m (p {$<$} .001); gait speed improved: 0.10 {$\pm$} 0.24 m/s (p {$=$} .04); and five-times sit-to-stand improved by 2.1 seconds (p {$<$} .001). Subjective measures of late-life function improved (5.2 {$\pm$} 7.1 points, p {$=$} .003), as did Patient-Reported Outcome Measurement Information Systems mental and physical health scores (5.0 {$\pm$} 5.7 and 4.4 {$\pm$} 5.0, both p {$<$} .001). Participants wore their Fitbit 93.9\% of all intervention days, and were overall satisfied with the trial (4.5/5.0, 1--5 low--high) and with Fitbit (4.0/5.0). \par \emph{Conclusions:} A multicomponent obesity intervention incorporating a wearable device is feasible and acceptable to older adults with obesity, and potentially holds promise in enhancing health. }, } @Article{seo:theraband, author = {Lillian M. Seo and Curtis L. Petersen and Ryan J. Halter and David F. Kotz and Karen L. Fortuna and John A. Batsis}, title = {{Usability Assessment of a Bluetooth-Enabled Resistance Exercise Band Among Young Adults}}, journal = {Health Technology}, year = 2021, month = {April}, volume = 5, number = 4, publisher = { AME Publishing}, copyright = {Health Technology}, DOI = {10.21037/ht-20-22}, URL = {https://www.cs.dartmouth.edu/~kotz/research/seo-theraband/index.html}, abstract = { \emph{Background:} Resistance-based exercises effectively enhance muscle strength, which is especially important in older populations as it reduces the risk of disability. Our group developed a Bluetooth-enabled handle for resistance exercise bands that wirelessly transmits relative force data through low-energy Bluetooth to a local smartphone or similar device. We present a usability assessment that evaluates an exercise system featuring a novel Bluetooth-enabled resistance exercise band, ultimately intended to expand the accessibility of resistance training through technology-enhanced home-based exercise programs for older adults. Although our target population is older adults, we assess the user experience among younger adults as a convenient and meaningful starting point in the testing and development of our device. \par \emph{Methods:} There were 32 young adults participating in three exercise sessions with the exercise band, after which each completed an adapted version of the Usefulness, Satisfaction, and Ease (USE) questionnaire to characterize the exercise system's strengths and weaknesses in usability. \par \emph{Results:} Questionnaire data reflected a positive and consistent user experience, with all 20 items receiving mean scores greater than 5.0 on a seven-point Likert scale. There were no specific areas of significant weakness in the device's user experience. \par \emph{Conclusions:} The positive reception among young adults is a promising indication that the device can be successfully incorporated into exercise interventions and that the system can be further developed and tested for the target population of older adults.}, } @Article{batsis:barriers, author = {John Batsis and Auden C. McClure and Aaron B. Weintraub and Diane Sette and Sivan Rotenberg and Courtney J. Stevens and Diane Gilbert-Diamond and David F. Kotz and Stephen J. Bartels and Summer B. Cook and Richard I. Rothstein}, title = {{Barriers and facilitators in implementing a pilot, pragmatic, telemedicine-delivered healthy lifestyle program for obesity management in a rural, academic obesity clinic}}, journal = {Implementation Science Communications}, year = 2020, month = {September}, volume = 1, articleno = 83, numpages = 9, publisher = {BMC}, copyright = {the authors}, DOI = {10.1186/s43058-020-00075-9}, URL = {https://www.cs.dartmouth.edu/~kotz/research/batsis-barriers/index.html}, abstract = {Few evidence-based strategies are specifically tailored for disparity populations such as rural adults. Two-way video-conferencing using telemedicine can potentially surmount geographic barriers that impede participation in high-intensity treatment programs offering frequent visits to clinic facilities. We aimed to understand barriers and facilitators of implementing a telemedicine-delivered tertiary-care, rural academic weight-loss program for the management of obesity.}, } @Article{batsis:mowi, author = {John Batsis and Stephen Bartels and Rachel Dokko and Alexandra Zagaria and John Naslund and Elizabeth Carpenter-Song and David Kotz}, title = {{Opportunities to Improve a Mobile Obesity Wellness Intervention for Rural Older Adults with Obesity}}, journal = {Journal of Community Health}, year = 2020, month = {February}, volume = 45, number = 1, pages = {194--200}, publisher = {Springer}, copyright = {Springer}, DOI = {10.1007/s10900-019-00720-y}, PMID = 31486958, URL = {https://www.cs.dartmouth.edu/~kotz/research/batsis-mowi/index.html}, abstract = {Older adults with obesity are at a high risk of decline, particularly in rural areas. Our study objective was to gain insights into how a potential Mobile Health Obesity Wellness Intervention (MOWI) in rural older adults with obesity, consisting of nutrition and exercise sessions, could be helpful to improve physical function. A qualitative methods study was conducted in a rural community, community-based aging center. Four community leaders, 7 clinicians and 29 patient participants underwent focus groups and semi-structured interviews. All participants had a favorable view of MOWI and saw its potential to improve health and create accountability. Participants noted that MOWI could overcome geographic barriers and provided feedback about components that could improve implementation. There was expressed enthusiasm over its potential to improve health. The use of technology in older adults with obesity in rural areas has considerable promise. There is potential that this intervention could potentially extend to distant areas in rural America that can surmount accessibility barriers. If successful, this intervention could potentially alter healthcare delivery by enhancing health promotion in a remote, geographically constrained communities. MOWI has the potential to reach older adults with obesity using novel methods in geographically isolated regions.}, } @Article{mishra:jcommodity, author = {Varun Mishra and Gunnar Pope and Sarah Lord and Stephanie Lewia and Byron Lowens and Kelly Caine and Sougata Sen and Ryan Halter and David Kotz}, title = {{Continuous Detection of Physiological Stress with Commodity Hardware}}, journal = {ACM Transactions on Computing for Healthcare (HEALTH)}, year = 2020, month = {April}, volume = 1, number = 2, articleno = 8, numpages = 30, publisher = {ACM}, copyright = {the authors}, DOI = {10.1145/3361562}, URL = {https://www.cs.dartmouth.edu/~kotz/research/mishra-jcommodity/index.html}, abstract = {Timely detection of an individual's stress level has the potential to improve stress management, thereby reducing the risk of adverse health consequences that may arise due to mismanagement of stress. Recent advances in wearable sensing have resulted in multiple approaches to detect and monitor stress with varying levels of accuracy. The most accurate methods, however, rely on clinical-grade sensors to measure physiological signals; they are often bulky, custom made, and expensive, hence limiting their adoption by researchers and the general public. In this article, we explore the viability of commercially available off-the-shelf sensors for stress monitoring. The idea is to be able to use cheap, nonclinical sensors to capture physiological signals and make inferences about the wearer's stress level based on that data. We describe a system involving a popular off-the-shelf heart rate monitor, the Polar H7; we evaluated our system with 26 participants in both a controlled lab setting with three well-validated stress-inducing stimuli and in free-living field conditions. Our analysis shows that using the off-the-shelf sensor alone, we were able to detect stressful events with an F1-score of up to 0.87 in the lab and 0.66 in the field, on par with clinical-grade sensors.}, } @Article{petersen:design, author = {Curtis Lee Petersen and Ryan Halter and David Kotz and Lorie Loeb and Summer Cook and Dawna Pidgeon and Brock C. Christensen and John A. Batsis}, title = {{Using Natural Language Processing and Sentiment Analysis to Augment Traditional User-Centered Design: Development and Usability Study}}, journal = {JMIR mHealth and uHealth}, year = 2020, month = {August}, volume = 8, number = 8, articleno = {e16862}, numpages = 13, publisher = {JMIR Publications}, copyright = {the authors}, DOI = {10.2196/16862}, URL = {https://www.cs.dartmouth.edu/~kotz/research/petersen-design/index.html}, abstract = {\emph{Background:} Sarcopenia, defined as the age-associated loss of muscle mass and strength, can be effectively mitigated through resistance-based physical activity. With compliance at approximately 40\% for home-based exercise prescriptions, implementing a remote sensing system would help patients and clinicians to better understand treatment progress and increase compliance. The inclusion of end users in the development of mobile apps for remote-sensing systems can ensure that they are both user friendly and facilitate compliance. With advancements in natural language processing (NLP), there is potential for these methods to be used with data collected through the user-centered design process.\par \emph{Objective:} This study aims to develop a mobile app for a novel device through a user-centered design process with both older adults and clinicians while exploring whether data collected through this process can be used in NLP and sentiment analysis.\par \emph{Methods:} Through a user-centered design process, we conducted semistructured interviews during the development of a geriatric-friendly Bluetooth-connected resistance exercise band app. We interviewed patients and clinicians at weeks 0, 5, and 10 of the app development. Each semistructured interview consisted of heuristic evaluations, cognitive walkthroughs, and observations. We used the Bing sentiment library for a sentiment analysis of interview transcripts and then applied NLP-based latent Dirichlet allocation (LDA) topic modeling to identify differences and similarities in patient and clinician participant interviews. Sentiment was defined as the sum of positive and negative words (each word with a +1 or --1 value). To assess utility, we used quantitative assessment questionnaires---System Usability Scale (SUS) and Usefulness, Satisfaction, and Ease of use (USE). Finally, we used multivariate linear models---adjusting for age, sex, subject group (clinician vs patient), and development---to explore the association between sentiment analysis and SUS and USE outcomes.\par \emph{Results:} The mean age of the 22 participants was 68 (SD 14) years, and 17 (77\%) were female. The overall mean SUS and USE scores were 66.4 (SD 13.6) and 41.3 (SD 15.2), respectively. Both patients and clinicians provided valuable insights into the needs of older adults when designing and building an app. The mean positive-negative sentiment per sentence was 0.19 (SD 0.21) and 0.47 (SD 0.21) for patient and clinician interviews, respectively. We found a positive association with positive sentiment in an interview and SUS score ({$\textbeta$}{$=$}1.38; 95\% CI 0.37 to 2.39; P{$=$}.01). There was no significant association between sentiment and the USE score. The LDA analysis found no overlap between patients and clinicians in the 8 identified topics.\par \emph{Conclusions:} Involving patients and clinicians allowed us to design and build an app that is user friendly for older adults while supporting compliance. This is the first analysis using NLP and usability questionnaires in the quantification of user-centered design of technology for older adults.}, } @Article{rauch:wtp, author = {Vanessa K. Rauch and Meredith Roderka and Auden C. McClure and Aaron B. Weintraub and Kevin Curtis and David F. Kotz and Richard I. Rothstein and John A. Batsis}, title = {{Willingness to pay for a telemedicine-delivered healthy lifestyle programme}}, journal = {Journal of Telemedicine and Telecare}, year = 2020, month = {June}, publisher = {Sage}, copyright = {the authors}, DOI = {10.1177/1357633X20943337}, PMID = 32781892, URL = {https://www.cs.dartmouth.edu/~kotz/research/rauch-wtp/index.html}, abstract = { \emph{Introduction:} Effective weight-management interventions require frequent interactions with specialised multidiscipli- nary teams of medical, nutritional and behavioural experts to enact behavioural change. However, barriers that exist in rural areas, such as transportation and a lack of specialised services, can prevent patients from receiving quality care. \par \emph{Methods:} We recruited patients from the Dartmouth-Hitchcock Weight \& Wellness Center into a single-arm, non- randomised study of a remotely delivered 16-week evidence-based healthy lifestyle programme. Every 4 weeks, partic- ipants completed surveys that included their willingness to pay for services like those experienced in the intervention. A two-item Willingness-to-Pay survey was administered to participants asking about their willingness to trade their face- to-face visits for videoconference visits based on commute and copay.\par \emph{Results:} Overall, those with a travel duration of 31--45 min had a greater willingness to trade in-person visits for telehealth than any other group. Participants who had a travel duration less than 15 min, 16--30 min and 46--60 min experienced a positive trend in willingness to have telehealth visits until Week 8, where there was a general negative trend in willingness to trade in-person visits for virtual. Participants believed that telemedicine was useful and helpful.\par \emph{Conclusions:} In rural areas where patients travel 30--45 min a telemedicine-delivered, intensive weight-loss interven- tion may be a well-received and cost-effective way for both patients and the clinical care team to connect.}, } @PhdThesis{peters:thesis, author = {Travis Peters}, title = {{Trustworthy Wireless Personal Area Networks}}, school = {Dartmouth Computer Science}, year = 2020, month = {August}, copyright = {the author}, address = {Hanover, NH}, URL = {https://www.cs.dartmouth.edu/~kotz/research/peters-thesis/index.html}, note = {Available as Dartmouth Computer Science Technical Report TR2020-878}, abstract = {\par In the Internet of Things (IoT), everyday objects are equipped with the ability to compute and communicate. These smart things have invaded the lives of everyday people, being constantly carried or worn on our bodies, and entering into our homes, our healthcare, and beyond. This has given rise to wireless networks of smart, connected, always-on, personal things that are constantly around us, and have unfettered access to our most personal data as well as all of the other devices that we own and encounter throughout our day. It should, therefore, come as no surprise that our personal devices and data are frequent targets of ever-present threats. Securing these devices and networks, however, is challenging. In this dissertation, we outline three critical problems in the context of Wireless Personal Area Networks (WPANs) and present our solutions to these problems. \par First, I present our Trusted I/O solution (BASTION-SGX) for protecting sensitive user data transferred between wirelessly connected (Bluetooth) devices. This work shows how in-transit data can be protected from privileged threats, such as a compromised OS, on commodity systems. I present insights into the Bluetooth architecture, Intel's Software Guard Extensions (SGX), and how a Trusted I/O solution can be engineered on commodity devices equipped with SGX. \par Second, I present our work on AMULET and how we successfully built a wearable health hub that can run multiple health applications, provide strong security properties, and operate on a single charge for weeks or even months at a time. I present the design and evaluation of our highly efficient event-driven programming model, the design of our low-power operating system, and developer tools for profiling ultra-low-power applications at compile time. \par Third, I present a new approach (VIA) that helps devices at the center of WPANs (e.g., smartphones) to verify the authenticity of interactions with other devices. This work builds on past work in anomaly detection techniques and shows how these techniques can be applied to Bluetooth network traffic. Specifically, we show how to create normality models based on fine- and course-grained insights from network traffic, which can be used to verify the authenticity of future interactions. }, } @Article{batsis:amulet-use, author = {John A. Batsis and Alexandra B. Zagaria and Ryan J. Halter and George G. Boateng and Patrick Proctor and Stephen J. Bartels and David Kotz}, title = {{Use of Amulet in behavioral change for geriatric obesity management}}, journal = {Journal of Digital Health}, year = 2019, month = {June}, volume = 5, pages = {1--7}, publisher = {Sage}, copyright = {the authors}, DOI = {10.1177/2055207619858564}, URL = {https://www.cs.dartmouth.edu/~kotz/research/batsis-amulet-use/index.html}, abstract = {Background: Obesity in older adults is a significant public health concern. Weight-loss interventions are known to improve physical function but risk the development of sarcopenia. Mobile health devices have the potential to augment existing interventions and, if designed accordingly, could improve one's physical activity and strength in routine physical activity interventions. Methods and results: We present Amulet, a mobile health device that has the capability of engaging patients in physical activity. The purpose of this article is to discuss the development of applications that are tailored to older adults with obesity, with the intention to engage and improve their health. Conclusions: Using a team-science approach, Amulet has the potential, as an open-source mobile health device, to tailor activity interventions to older adults.}, } @Article{batsis:change, author = {John A. Batsis and John A. Naslund and Alexandra B. Zagaria and David Kotz and Rachel Dokko and Stephen J. Bartels and Elizabeth Carpenter-Song}, title = {{Technology for Behavioral Change in Rural Older Adults with Obesity}}, journal = {Journal of Nutrition in Gerontology and Geriatrics}, year = 2019, month = {April}, volume = 38, number = 2, pages = {130--148}, publisher = {Taylor \& Francis}, copyright = {Taylor \& Francis Group, LLC}, DOI = {10.1080/21551197.2019.1600097}, URL = {https://www.cs.dartmouth.edu/~kotz/research/batsis-change/index.html}, abstract = {Background: Mobile health (mHealth) technologies comprise a multidisciplinary treatment strategy providing potential solutions for overcoming challenges of successfully delivering health promotion interventions in rural areas. We evaluated the potential of using technology in a high-risk population. \par Methods: We conducted a convergent, parallel mixed-methods study using semi-structured interviews, focus groups, and self-reported questionnaires, using purposive sampling of 29 older adults, 4 community leaders and 7 clinicians in a rural setting. We developed codes informed by thematic analysis and assessed the quantitative data using descriptive statistics. \par Results: All groups expressed that mHealth could improve health behaviors. Older adults were optimistic that mHealth could track health. Participants believed they could improve patient insight into health, motivating change and assuring accountability. Barriers to using technology were described, including infrastructure. \par Conclusions: Older rural adults with obesity expressed excitement about the use of mHealth technologies to improve their health, yet barriers to implementation exist.}, } @Article{batsis:development, author = {John A. Batsis and George G. Boateng and Lillian M. Seo and Curtis L. Petersen and Karen L. Fortuna and Emily V. Wechsler and Ronald J. Peterson and Summer B. Cook and Dawna Pidgeon and Rachel S. Dokko and Ryan J. Halter and David F. Kotz}, title = {{Development and Usability Assessment of a Connected Resistance Exercise Band Application for Strength-Monitoring}}, journal = {World Academy of Science, Engineering and Technology}, year = 2019, month = {June}, volume = 13, number = 5, pages = {340--348}, publisher = {World Academy of Science, Engineering and Technology}, copyright = {World Academy of Science, Engineering and Technology}, PMID = 31205628, URL = {https://www.cs.dartmouth.edu/~kotz/research/batsis-development/index.html}, note = {Presented at the International Conference on Body Area Networks (ICBAN)}, abstract = {Resistance exercise bands are a core component of any physical activity strengthening program. Strength training can mitigate the development of sarcopenia, the loss of muscle mass or strength and function with aging. Yet, the adherence of such behavioral exercise strategies in a home-based setting is fraught with issues of monitoring and compliance. Our group developed a Bluetooth-enabled resistance exercise band capable of transmitting data to an open-source platform. In this work, we developed an application to capture this information in real-time and conducted three usability studies in two mixed-aged groups of participants (n{$=$}6 each) and a group of older adults with obesity participating in a weight-loss intervention (n{$=$}20). The system was favorable, acceptable and provided iterative information that could assist in future deployment on ubiquitous platforms. Our formative work provides the foundation to deliver home-based monitoring interventions in a high-risk, older adult population.}, } @Article{batsis:feasibility, author = {John A. Batsis and Auden C. McClure and Aaron B. Weintraub and David F. Kotz and Sivan Rotenberg and Summer B. Cook and Diane Gilbert-Diamond and Kevin Curtis and Courtney J. Stevens and Diane Sette and Richard I. Rothstein}, title = {{Feasibility and acceptability of a rural, pragmatic, telemedicine-delivered healthy lifestyle programme}}, journal = {Obesity Science \& Practice}, year = 2019, month = {December}, volume = 5, number = 6, pages = {521--530}, publisher = {Wiley}, copyright = {the authors}, DOI = {10.1002/osp4.366}, URL = {https://www.cs.dartmouth.edu/~kotz/research/batsis-feasibility/index.html}, abstract = {Background: The public health crisis of obesity leads to increasing morbidity that are even more profound in certain populations such as rural adults. Live, two-way video-conferencing is a modality that can potentially surmount geographic barriers and staffing shortages. Methods: Patients from the Dartmouth-Hitchcock Weight and Wellness Center were recruited into a pragmatic, single-arm, nonrandomized study of a remotely delivered 16-week evidence-based healthy lifestyle programme. Patients were provided hardware and appropriate software allowing for remote participation in all sessions, outside of the clinic setting. Our primary outcomes were feasibility and acceptability of the telemedicine intervention, as well as potential effectiveness on anthropometric and functional measures. Results: Of 62 participants approached, we enrolled 37, of which 27 completed at least 75\% of the 16-week programme sessions (27\% attrition). Mean age was 46.9 +/- 11.6 years (88.9\% female), with a mean body mass index of 41.3 +/- 7.1 kg/m2 and mean waist circumference of 120.7 +/- 16.8 cm. Mean patient participant satisfaction regarding the telemedicine approach was favourable (4.48 +/- 0.58 on 1-5 Likert scale -- low to high) and 67.6/75 on standardized questionnaire. Mean weight loss at 16 weeks was 2.22 +/- 3.18 kg representing a 2.1\% change (P {$<$} .001), with a loss in waist circumference of 3.4\% (P {$=$} .001). Fat mass and visceral fat were significantly lower at 16 weeks (2.9\% and 12.5\%; both P less than .05), with marginal improvement in appendicular skeletal muscle mass (1.7\%). In the 30-second sit-to-stand test, a mean improvement of 2.46 stands (P {$=$} .005) was observed. Conclusion: A telemedicine-delivered, intensive weight loss intervention is feasible, acceptable, and potentially effective in rural adults seeking weight loss.}, } @InProceedings{boateng:experience, author = {George Boateng and Vivian Genaro Motti and Varun Mishra and John A. Batsis and Josiah Hester and David Kotz}, title = {{Experience: Design, Development and Evaluation of a Wearable Device for mHealth Applications}}, booktitle = {{Proceedings of the International Conference on Mobile Computing and Networking (MobiCom)}}, year = 2019, month = {October}, articleno = 31, numpages = 14, publisher = {ACM}, copyright = {ACM}, DOI = {10.1145/3300061.3345432}, URL = {https://www.cs.dartmouth.edu/~kotz/research/boateng-experience/index.html}, abstract = {Wrist-worn devices hold great potential as a platform for mobile health (mHealth) applications because they comprise a familiar, convenient form factor and can embed sensors in proximity to the human body. Despite this potential, however, they are severely limited in battery life, storage, bandwidth, computing power, and screen size. In this paper, we describe the experience of the research and development team designing, implementing and evaluating Amulet -- an open-hardware, open-software wrist-worn computing device -- and its experience using Amulet to deploy mHealth apps in the field. In the past five years the team conducted 11 studies in the lab and in the field, involving 204 participants and collecting over 77,780 hours of sensor data. We describe the technical issues the team encountered and the lessons they learned, and conclude with a set of recommendations. We anticipate the experience described herein will be useful for the development of other research-oriented computing platforms. It should also be useful for researchers interested in developing and deploying mHealth applications, whether with the Amulet system or with other wearable platforms.}, } @Article{greene:sharehealth, author = {Emily Greene and Patrick Proctor and David Kotz}, title = {{Secure Sharing of mHealth Data Streams through Cryptographically-Enforced Access Control}}, journal = {Journal of Smart Health}, year = 2019, month = {April}, volume = 12, pages = {49--65}, publisher = {Elsevier}, copyright = {Elsevier}, DOI = {10.1016/j.smhl.2018.01.003}, URL = {https://www.cs.dartmouth.edu/~kotz/research/greene-sharehealth/index.html}, abstract = {Owners of mobile-health apps and devices often want to share their mHealth data with others, such as physicians, therapists, coaches, and caregivers. For privacy reasons, however, they typically want to share a limited subset of their information with each recipient according to their preferences. In this paper, we introduce ShareHealth, a scalable, usable, and practical system that allows mHealth-data owners to specify access-control policies and to cryptographically enforce those policies so that only parties with the proper corresponding permissions are able to decrypt data. The design and prototype implementation of this system make three contributions: (1) they apply cryptographically-enforced access-control measures to stream-based (specifically mHealth) data, (2) they recognize the temporal nature of mHealth data streams and support revocation of access to part or all of a data stream, and (3) they depart from the vendor- and device-specific silos of mHealth data by implementing a secure end-to-end system that can be applied to data collected from a variety of mHealth apps and devices.}, } @InProceedings{kotz:amulet19, author = {David Kotz}, title = {{Amulet: an open-source wrist-worn platform for mHealth research and education}}, booktitle = {{Proceedings of the Workshop on Networked Healthcare Technology (NetHealth)}}, year = 2019, month = {January}, pages = {891--897}, publisher = {IEEE}, copyright = {IEEE}, DOI = {10.1109/COMSNETS.2019.8711407}, URL = {https://www.cs.dartmouth.edu/~kotz/research/kotz-amulet19/index.html}, abstract = {The advent of mobile and wearable computing technology has opened up tremendous opportunities for health and wellness applications. It is increasingly possible for individuals to wear devices that can sense their physiology or health-related behaviors, collecting valuable data in support of diagnosis, treatment, public health, or other applications. From a researcher's point of view, the commercial availability of these ``mHealth'' devices has made it feasible to conduct scientific studies of health conditions and to explore health-related interventions. It remains difficult, however, to conduct systems work or other experimental research involving the hardware, software, security, and networking aspects of mobile and wearable technology. In this paper we describe the Amulet platform, an open-hardware, open-software wrist-worn computing device designed specifically for mHealth applications. Our position is that the Amulet is an inexpensive platform for research and education, and we encourage the mHealth community to explore its potential.}, } @Article{batsis:usability, author = {John A. Batsis and Alexandra Zagaria and David F. Kotz and Stephen J. Bartels and George G. Boateng and Patrick O. Proctor and Ryan J. Halter and Elizabeth A. Carpenter-Song}, title = {{Usability evaluation for the Amulet wearable device in rural older adults with obesity}}, journal = {Gerontechnology}, year = 2018, month = {October}, volume = 17, number = 3, pages = {151--159}, publisher = {International Society for Gerontechnology}, copyright = {International Society for Gerontechnology}, DOI = {10.4017/gt.2018.17.3.003.00}, URL = {https://www.cs.dartmouth.edu/~kotz/research/batsis-usability/index.html}, abstract = {Mobile health (mHealth) interventions hold the promise of augmenting existing health promotion interventions. Older adults present unique challenges in advancing new models of health promotion using technology including sensory limitations and less experience with mHealth, underscoring the need for specialized usability testing. We use an open-source mHealth device as a case example for its integration in a newly designed health services intervention. We performed a convergent, parallel mixed-methods study including semi-structured interviews, focus groups, and questionnaires, using purposive sampling of 29 older adults, 4 community leaders, and 7 clinicians in a rural setting. We transcribed the data, developed codes informed by thematic analysis using inductive and deductive methods, and assessed the quantitative data using descriptive statistics. Our results suggest the importance of end-users in user-centered design of mHealth devices and that aesthetics are critically important. The prototype could potentially be feasibly integrated within health behavior interventions. Centralized dashboards were desired by all participants and ecological momentary assessment could be an important part of monitoring. Concerns of mHealth, including the prototype device, include the device's accuracy, its intrusiveness in daily life and privacy. Formative evaluations are critically important prior to deploying large-scale interventions.}, } @InProceedings{boateng:geriactive, author = {George Boateng and John A. Batsis and Patrick Proctor and Ryan Halter and David Kotz}, title = {{GeriActive: Wearable App for Monitoring and Encouraging Physical Activity among Older Adults}}, booktitle = {{Proceedings of the IEEE Conference on Body Sensor Networks (BSN)}}, year = 2018, month = {March}, pages = {46--49}, publisher = {IEEE}, copyright = {IEEE}, DOI = {10.1109/BSN.2018.8329655}, URL = {https://www.cs.dartmouth.edu/~kotz/research/boateng-geriactive/index.html}, abstract = {The ability to monitor a person's level of daily activity can inform self-management of physical activity and assist in augmenting behavioral interventions. For older adults, the importance of regular physical activity is critical to reduce the risk of long-term disability. In this work, we present GeriActive, an application on the Amulet wrist-worn device that monitors in real time older adults' daily activity levels (low, moderate and vigorous), which we categorized using metabolic equivalents (METs). The app implements an activity-level detection model we developed using a linear Support Vector Machine (SVM). We trained our model using data from volunteer subjects (n{$=$}29) who performed common physical activities (sit, stand, lay down, walk and run) and obtained an accuracy of 94.3\% with leave-one-subject-out (LOSO) cross-validation. We ran a week-long field study to evaluate the usability and battery life of the GeriActive system where 5 older adults wore the Amulet as it monitored their activity level. Their feedback showed that our system has the potential to be usable and useful. Our evaluation further revealed a battery life of at least 1 week. The results are promising, indicating that the app may be used for activity-level monitoring by individuals or researchers for health delivery interventions that could improve the health of older adults.}, } @InProceedings{hardin:mpu, author = {Taylor Hardin and Ryan Scott and Patrick Proctor and Josiah Hester and Jacob Sorber and David Kotz}, title = {{Application Memory Isolation on Ultra-Low-Power MCUs}}, booktitle = {{Proceedings of the USENIX Annual Technical Conference (USENIX ATC)}}, year = 2018, month = {July}, pages = {127--132}, publisher = {USENIX Association}, copyright = {the authors}, URL = {https://www.cs.dartmouth.edu/~kotz/research/hardin-mpu/index.html}, abstract = {The proliferation of applications that handle sensitive user data on wearable platforms generates a critical need for embedded systems that offer strong security without sacrificing flexibility and long battery life. To secure sensitive information, such as health data, ultra-low-power wearables must isolate applications from each other and protect the underlying system from errant or malicious application code. These platforms typically use microcontrollers that lack sophisticated Memory Management Units (MMU). Some include a Memory Protection Unit (MPU), but current MPUs are inadequate to the task, leading platform developers to software-based memory-protection solutions. In this paper, we present our memory isolation technique, which leverages compiler inserted code and MPU-hardware support to achieve better runtime performance than software-only counterparts.}, } @InProceedings{mishra:commodity, author = {Varun Mishra and Gunnar Pope and Sarah Lord and Stephanie Lewia and Byron Lowens and Kelly Caine and Sougata Sen and Ryan Halter and David Kotz}, title = {{The Case for a Commodity Hardware Solution for Stress Detection}}, booktitle = {{Proceedings of the Workshop on Mental Health: Sensing \& Intervention}}, year = 2018, month = {October}, pages = {1717--1728}, publisher = {ACM}, copyright = {ACM}, DOI = {10.1145/3267305.3267538}, URL = {https://www.cs.dartmouth.edu/~kotz/research/mishra-commodity/index.html}, abstract = {Timely detection of an individual's stress level has the potential to expedite and improve stress management, thereby reducing the risk of adverse health consequences that may arise due to unawareness or mismanagement of stress. Recent advances in wearable sensing have resulted in multiple approaches to detect and monitor stress with varying levels of accuracy. The most accurate methods, however, rely on clinical grade sensors strapped to the user. These sensors measure physiological signals of a person and are often bulky, custom-made, expensive, and/or in limited supply, hence limiting their large-scale adoption by researchers and the general public. In this paper, we explore the viability of commercially available off-the-shelf sensors for stress monitoring. The idea is to be able to use cheap, non-clinical sensors to capture physiological signals, and make inferences about the wearer's stress level based on that data. In this paper, we describe a system involving a popular off-the-shelf heart-rate monitor, the Polar H7; we evaluated our system in a lab setting with three well-validated stress-inducing stimuli with 26 participants. Our analysis shows that using the off-the-shelf sensor alone, we were able to detect stressful events with an F1 score of 0.81, on par with clinical-grade sensors.}, } @TechReport{mishra:ema-tr, author = {Varun Mishra and Byron Lowens and Sarah Lord and Kelly Caine and David Kotz}, title = {{Investigating Contextual Cues as Indicators for EMA Delivery}}, institution = {Dartmouth Computer Science}, year = 2018, month = {April}, number = {TR2018-842}, copyright = {the authors}, URL = {https://www.cs.dartmouth.edu/~kotz/research/mishra-ema-tr/index.html}, abstract = {In this work, we attempt to determine whether the contextual information of a participant can be used to predict whether the participant will respond to a particular Ecological Momentary Assessment (EMA) prompt. We use a publicly available dataset for our work, and find that by using basic contextual features about the participant's activity, conversation status, audio, and location, we can predict whether an EMA prompt triggered at a particular time will be answered with a precision of 0.647, which is significantly higher than a baseline precision of 0.410. Using this knowledge, the researchers conducting field studies can efficiently schedule EMA prompts and achieve higher response rates.}, } @InProceedings{peterson:chase, author = {Curtis L. Petersen and Emily V. Wechsler and Ryan J. Halter and George G. Boateng and Patrick O. Proctor and David F. Kotz and Summer B. Cook and John A. Batsis}, title = {{Detection and Monitoring of Repetitions Using an mHealth-Enabled Resistance Band}}, booktitle = {{Proceedings of the IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)}}, year = 2018, month = {September}, pages = {22--24}, publisher = {ACM}, copyright = {ACM}, DOI = {10.1145/3278576.3278586}, URL = {https://www.cs.dartmouth.edu/~kotz/research/peterson-chase/index.html}, abstract = {Sarcopenia is defined as an age-related loss of muscle mass and strength which impairs physical function leading to disability and frailty. Resistance exercises are effective treatments for sarcopenia and are critical in mitigating weight-loss induced sarcopenia in older adults attempting to lose weight. Yet, adherence to home-based regimens, which is a cornerstone to lifestyle therapies, is poor and cannot be ascertained by clinicians as no objective methods exist to determine patient compliance outside of a supervised setting. Our group developed a Bluetooth connected resistance band that tests the ability to detect exercise repetitions. We recruited 6 patients aged 65 years and older and recorded 4 specific, physical therapist-led exercises. Three blinded reviewers examined the findings and we also applied a peak finding algorithm to the data. There were 16.6 repetitions per exercise across reviewers, with an intraclass correlation of 0.912 (95\%CI: 0.853--0.953, p{$<$}0.001) between reviewers and the algorithm. Using this novel resistance band, we feasibly detected repetition of exercises in older adults.}, } @InProceedings{pope:eda-bsn, author = {Gunnar C. Pope and Varun Mishra and Stephanie Lewia and Byron Lowens and David Kotz and Sarah Lord and Ryan Halter}, title = {{An Ultra-Low Resource Wearable EDA Sensor Using Wavelet Compression}}, booktitle = {{Proceedings of the IEEE Conference on Body Sensor Networks (BSN)}}, year = 2018, month = {March}, pages = {193--196}, publisher = {IEEE}, copyright = {IEEE}, DOI = {10.1109/BSN.2018.8329691}, URL = {https://www.cs.dartmouth.edu/~kotz/research/pope-eda-bsn/index.html}, abstract = {This study presents an ultra-low resource platform for physiological sensing that uses on-chip wavelet compression to enable long-term recording of electrodermal activity (EDA) within a 64kB microcontroller. The design is implemented on a wearable platform and provides improvements in size and power compared to existing wearable technologies and was used in a lab setting to monitor EDA of 27 participants throughout a stress induction protocol. We demonstrate the device's sensitivity to stress induction by providing descriptive statistics of 8 common EDA signal features for each stressor of the experiment. To the best of our knowledge, this is the first time a generic, 16-bit microcontroller (MCU) has been used to record real-time physiological signals on a wearable platform without the use of external memory chips or wireless transmission for extended periods of time. The compression techniques described can lead to reductions in size, power, and cost of wearable biosensors with little or no modifications to existing sensor hardware and could be valuable for applications interested in monitoring long-term physiological trends at lower data rates and memory requirements.}, } @Misc{kotz:patent9936877, author = {David Kotz and Ryan Halter and Cory Cornelius and Jacob Sorber and Minho Shin and Ronald Peterson and Shrirang Mare and Aarathi Prasad and Joseph Skinner and Andr{\'{e}}s Molina-Markham}, title = {{Wearable computing device for secure control of physiological sensors and medical devices, with secure storage of medical records, and bioimpedance biometric}}, howpublished = {U.S. Patent 9,936,877; International Patent Application WO2013096954A1}, year = 2018, month = {April}, day = 10, URL = {https://www.cs.dartmouth.edu/~kotz/research/kotz-patent9936877/index.html}, note = {This patent adds claims to its predecessor; Priority date 2011-12-23; Filed 2017-02-07; Issued 2018-04-10}, abstract = {A wearable master electronic device (Amulet) has a processor with memory, the processor coupled to a body-area network (BAN) radio and uplink radio. The device has firmware for BAN communications with wearable nodes to receive data, and in an embodiment, send configuration data. The device has firmware for using the uplink radio to download apps and configurations, and upload data to a server. An embodiment has accelerometers in Amulet and wearable node, and firmware for using accelerometer readings to determine if node and Amulet are worn by the same subject. Other embodiments use pulse sensors or microphones in the Amulet and node to both identify a subject and verify the Amulet and node are worn by the same subject. Another embodiment uses a bioimpedance sensor to identify the subject. The wearable node may be an insulin pump, chemotherapy pump, TENS unit, cardiac monitor, or other device.}, } @InProceedings{boateng:activityaware, author = {George Boateng and John A. Batsis and Ryan Halter and David Kotz}, title = {{ActivityAware: An App for Real-Time Daily Activity Level Monitoring on the Amulet Wrist-Worn Device}}, booktitle = {{Proceedings of the IEEE PerCom Workshop on Pervasive Health Technologies (PerHealth)}}, year = 2017, month = {March}, pages = {431--435}, publisher = {IEEE}, copyright = {IEEE}, DOI = {10.1109/PERCOMW.2017.7917601}, URL = {https://www.cs.dartmouth.edu/~kotz/research/boateng-activityaware/index.html}, abstract = {Physical activity helps reduce the risk of cardiovascular disease, hypertension and obesity. The ability to monitor a person's daily activity level can inform self-management of physical activity and related interventions. For older adults with obesity, the importance of regular, physical activity is critical to reduce the risk of long-term disability. In this work, we present ActivityAware, an application on the Amulet wrist-worn device that measures daily activity levels (sedentary, moderate and vigorous) of individuals, continuously and in real-time. The app implements an activity-level detection model, continuously collects acceleration data on the Amulet, classifies the current activity level, updates the day's accumulated time spent at that activity level, logs the data for later analysis, and displays the results on the screen. We developed an activity-level detection model using a Support Vector Machine (SVM). We trained our classifiers using data from a user study, where subjects performed the following physical activities: sit, stand, lay down, walk and run. With 10-fold cross validation and leave-one-subject-out (LOSO) cross validation, we obtained preliminary results that suggest accuracies up to 98\%, for n{$=$}14 subjects. Testing the ActivityAware app revealed a projected battery life of up to 4 weeks before needing to recharge. The results are promising, indicating that the app may be used for activity-level monitoring, and eventually for the development of interventions that could improve the health of individuals.}, } @InProceedings{boateng:stressaware, author = {George Boateng and David Kotz}, title = {{StressAware: An App for Real-Time Stress Monitoring on the Amulet Wearable Platform}}, booktitle = {{Proceedings of the IEEE MIT Undergraduate Research Technology Conference (URTC)}}, year = 2017, month = {January}, pages = {1--4}, publisher = {IEEE}, copyright = {IEEE}, DOI = {10.1109/URTC.2016.8284068}, URL = {https://www.cs.dartmouth.edu/~kotz/research/boateng-stressaware/index.html}, abstract = {Stress is the root cause of many diseases and unhealthy behaviors. Being able to monitor when and why a person is stressed could inform personal stress management as well as interventions when necessary. In this work, we present StressAware, an application on the Amulet wearable platform that classifies the stress level (low, medium, high) of individuals continuously and in real time using heart rate (HR) and heart-rate variability (HRV) data from a commercial heart-rate monitor. We developed our stress-detection model using a Support Vector Machine (SVM). We trained and tested our model using data from three sources and had the following preliminary results: PhysioNet, a public physiological database (94.5\% accurate with 10-fold cross validation), a field study (100\% accurate with 10-fold cross validation) and a lab study (64.3\% accurate with leave-one-out cross-validation). Testing the StressAware app revealed a projected battery life of up to 12 days. Also, the usability feedback from subjects showed that the Amulet has a potential to be used by people for monitoring their stress levels. The results are promising, indicating that the app may be used for stress detection, and eventually for the development of stress-related intervention that could improve the health of individuals.}, } @InProceedings{hardin:mobisys17, author = {Taylor Hardin and Josiah Hester and Patrick Proctor and Jacob Sorber and David Kotz}, title = {{Poster: Memory Protection in Ultra-Low-Power Multi-Application Wearables}}, booktitle = {{Proceedings of the ACM International Conference on Mobile Systems, Applications, and Services (MobiSys)}}, year = 2017, month = {June}, pages = 170, publisher = {ACM}, copyright = {ACM}, DOI = {10.1145/3081333.3089314}, URL = {https://www.cs.dartmouth.edu/~kotz/research/hardin-mobisys17/index.html}, abstract = {Ultra-low-power microcontrollers have historically not offered MPUs; only recently have MPUs become more prevalent, but many lack the functionality for sufficient memory management and protection. Thus, those who develop multi-application, multi-tenant platforms isolate applications using compile-time or run-time software sandboxing (e.g., AmuletOS), imposing limits on application developers and adding time/space overhead to running applications. We have developed methods, however, to leverage the limited MPUs and thereby reduce overhead cost by narrowing the use of software-based approaches.}, } @InProceedings{mishra:ema-workshop, author = {Varun Mishra and Byron Lowens and Sarah Lord and Kelly Caine and David Kotz}, title = {{Investigating Contextual Cues As Indicators for EMA Delivery}}, booktitle = {{Proceedings of the International Workshop on Smart and Ambient Notification and Attention Management (UbiTtention)}}, year = 2017, month = {September}, pages = {935--940}, publisher = {ACM}, copyright = {ACM}, location = {Maui, Hawaii}, DOI = {10.1145/3123024.3124571}, URL = {https://www.cs.dartmouth.edu/~kotz/research/mishra-ema-workshop/index.html}, abstract = {In this work, we attempt to determine whether the contextual information of a participant can be used to predict whether the participant will respond to a particular EMA trigger. We use a publicly available dataset for our work, and find that by using basic contextual features about the participant's activity, conversation status, audio, and location, we can predict if an EMA triggered at a particular time will be answered with a precision of 0.647, which is significantly higher than a baseline precision of 0.41. Using this knowledge, the researchers conducting field studies can efficiently schedule EMAs and achieve higher response rates.}, } @MastersThesis{boateng:msthesis, author = {George G. Boateng}, title = {{ActivityAware: Wearable System for Real-Time Physical Activity Monitoring among the Elderly}}, school = {Dartmouth Computer Science}, year = 2017, month = {May}, copyright = {George G. Boateng}, address = {Hanover, NH}, URL = {https://www.cs.dartmouth.edu/~kotz/research/boateng-msthesis/index.html}, note = {Available as Dartmouth Computer Science Technical Report TR2017-824}, abstract = {Physical activity helps reduce the risk of cardiovascular disease, hypertension and obesity. The ability to monitor a person's daily activity level can inform self-management of physical activity and related interventions. For older adults with obesity, the importance of regular, physical activity is critical to reduce the risk of long-term disability. In this work, we present ActivityAware, an application on the Amulet wrist-worn device that monitors the daily activity levels (low, moderate and vigorous) of older adults in real-time. The app continuously collects acceleration data on the Amulet, classifies the current activity level, updates the day's accumulated time spent at that activity level, displays the results on the screen and logs summary data for later analysis. \par The app implements an activity-level detection model we developed using a Linear Support Vector Machine (SVM). We trained our model using data from a user study, where subjects performed common physical activities (sit, stand, lay down, walk and run). We obtained accuracies up to 99.2\% and 98.5\% with 10-fold cross validation and leave-one-subject-out (LOSO) cross-validation respectively. We ran a week-long field study to evaluate the utility, usability and battery life of the ActivityAware system where 5 older adults wore the Amulet as it monitored their activity level. The utility evaluation showed that the app was somewhat useful in achieving the daily physical activity goal. The usability feedback showed that the ActivityAware system has the potential to be used by people for monitoring their activity levels. Our energy-efficiency evaluation revealed a battery life of at least 1 week before needing to recharge. The results are promising, indicating that the app may be used for activity-level monitoring by individuals or researchers for epidemiological studies, and eventually for the development of interventions that could improve the health of older adults.}, } @TechReport{greene:thesis, author = {Emily Greene}, title = {{ShareABEL: Secure Sharing of mHealth Data through Cryptographically-Enforced Access Control}}, institution = {Dartmouth College, Computer Science}, year = 2017, month = {July}, number = {TR2017-827}, copyright = {the author}, address = {Hanover, NH}, URL = {https://www.cs.dartmouth.edu/~kotz/research/greene-thesis/index.html}, abstract = {Owners of mobile-health apps and devices often want to share their mHealth data with others, such as physicians, therapists, coaches, and caregivers. For privacy reasons, however, they typically want to share a limited subset of their information with each recipient according to their preferences. In this paper, we introduce ShareABEL, a scalable, usable, and practical system that allows mHealth-data owners to specify access-control policies and to cryptographically enforce those policies so that only parties with the proper corresponding permissions are able to decrypt data. The design (and prototype implementation) of this system makes three contributions: (1) it applies cryptographically-enforced access-control measures to wearable healthcare data, which pose different challenges than Electronic Medical Records (EMRs), (2) it recognizes the temporal nature of mHealth data streams and supports revocation of access to part or all of a data stream, and (3) it departs from the vendor- and device-specific silos of mHealth data by implementing a secure end-to-end system that can be applied to data collected from a variety of mHealth apps and devices.}, } @TechReport{harmon:thesis, author = {David B. Harmon}, title = {{Cryptographic transfer of sensor data from the Amulet to a smartphone}}, institution = {Dartmouth College, Computer Science}, year = 2017, month = {May}, number = {TR2017-826}, copyright = {the author}, address = {Hanover, NH}, URL = {https://www.cs.dartmouth.edu/~kotz/research/harmon-thesis/index.html}, abstract = {The authenticity, confidentiality, and integrity of data streams from wearable healthcare devices are critical to patients, researchers, physicians, and others who depend on this data to measure the effectiveness of treatment plans and clinical trials. Many forms of mHealth data are highly sensitive; in the hands of unintended parties such data may reveal indicators of a patient's disorder, disability, or identity. Furthermore, if a malicious party tampers with the data, it can affect the diagnosis or treatment of patients, or the results of a research study. Although existing network protocols leverage encryption for confidentiality and integrity, network-level encryption does not provide end-to-end security from the device, through the smartphone and database, to downstream data consumers. In this thesis we provide a new open protocol that provides end-to-end authentication, confidentiality, and integrity for healthcare data in such a pipeline. \par We present and evaluate a prototype implementation to demonstrate this protocol's feasibility on low-power wearable devices, and present a case for the system's ability to meet critical security properties under a specific adversary model and trust assumptions.}, } @Misc{kotz:patent9595187, author = {David Kotz and Ryan Halter and Cory Cornelius and Jacob Sorber and Minho Shin and Ronald Peterson and Shrirang Mare and Aarathi Prasad and Joseph Skinner and Andr{\'{e}}s Molina-Markham}, title = {{Wearable computing device for secure control of physiological sensors and medical devices, with secure storage of medical records, and bioimpedance biometric}}, howpublished = {U.S. Patent 9,595,187; International Patent Application WO2013096954A1}, year = 2017, month = {March}, day = 14, URL = {https://www.cs.dartmouth.edu/~kotz/research/kotz-patent9595187/index.html}, note = {Priority date 2011-12-23; Filed 2012-12-24; Issued 2017-03-14}, abstract = {A wearable master electronic device (Amulet) has a processor with memory, the processor coupled to a body-area network (BAN) radio and uplink radio. The device has firmware for BAN communications with wearable nodes to receive data, and in an embodiment, send configuration data. The device has firmware for using the uplink radio to download apps and configurations, and upload data to a server. An embodiment has accelerometers in Amulet and wearable node, and firmware for using accelerometer readings to determine if node and Amulet are worn by the same subject. Other embodiments use pulse sensors or microphones in the Amulet and node to both identify a subject and verify the Amulet and node are worn by the same subject. Another embodiment uses a bioimpedance sensor to identify the subject. The wearable node may be an insulin pump, chemotherapy pump, TENS unit, cardiac monitor, or other device.}, } @InProceedings{hester:amulet-demo, author = {Josiah Hester and Travis Peters and Tianlong Yun and Ronald Peterson and Joseph Skinner and Bhargav Golla and Kevin Storer and Steven Hearndon and Sarah Lord and Ryan Halter and David Kotz and Jacob Sorber}, title = {{The Amulet Wearable Platform: Demo Abstract}}, booktitle = {{Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys)}}, year = 2016, month = {November}, pages = {290--291}, publisher = {ACM}, copyright = {ACM}, location = {Stanford, CA}, DOI = {10.1145/2994551.2996527}, URL = {https://www.cs.dartmouth.edu/~kotz/research/hester-amulet-demo/index.html}, abstract = {In this demonstration we present the Amulet Platform; a hardware and software platform for developing energy- and resource-efficient applications on multi-application wearable devices. This platform, which includes the Amulet Firmware Toolchain, the Amulet Runtime, the ARP-View graphical tool, and open reference hardware, efficiently protects applications from each other without MMU support, allows developers to interactively explore how their implementation decisions impact battery life without the need for hardware modeling and additional software development, and represents a new approach to developing long-lived wearable applications. We envision the Amulet Platform enabling long-duration experiments on human subjects in a wide variety of studies.}, } @InProceedings{hester:amulet, author = {Josiah Hester and Travis Peters and Tianlong Yun and Ronald Peterson and Joseph Skinner and Bhargav Golla and Kevin Storer and Steven Hearndon and Kevin Freeman and Sarah Lord and Ryan Halter and David Kotz and Jacob Sorber}, title = {{Amulet: An Energy-Efficient, Multi-Application Wearable Platform}}, booktitle = {{Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys)}}, year = 2016, month = {November}, pages = {216--229}, publisher = {ACM}, copyright = {ACM}, location = {Stanford, CA}, DOI = {10.1145/2994551.2994554}, URL = {https://www.cs.dartmouth.edu/~kotz/research/hester-amulet/index.html}, abstract = {Wearable technology enables a range of exciting new applications in health, commerce, and beyond. For many important applications, wearables must have battery life measured in weeks or months, not hours and days as in most current devices. Our vision of wearable platforms aims for long battery life but with the flexibility and security to support multiple applications. To achieve long battery life with a workload comprising apps from multiple developers, these platforms must have robust mechanisms for app isolation and developer tools for optimizing resource usage. \par We introduce the Amulet Platform for constrained wearable devices, which includes an ultra-low-power hardware architecture and a companion software framework, including a highly efficient event-driven programming model, low-power operating system, and developer tools for profiling ultra-low-power applications at compile time. We present the design and evaluation of our prototype Amulet hardware and software, and show how the framework enables developers to write energy-efficient applications. Our prototype has battery lifetime lasting weeks or even months, depending on the application, and our interactive resource-profiling tool predicts battery lifetime within 6-10\% of the measured lifetime.}, } @TechReport{boateng:stressaware-thesis, author = {George G. Boateng}, title = {{StressAware: App for Continuously Measuring and Monitoring Stress Levels in Real Time on the Amulet Wearable Device}}, institution = {Dartmouth Computer Science}, year = 2016, month = {May}, number = {TR2016-802}, copyright = {the author}, address = {Hanover, NH}, URL = {https://www.cs.dartmouth.edu/~kotz/research/boateng-stressaware-thesis/index.html}, abstract = {Stress is the root cause of many diseases. Being able to monitor when and why a person is stressed could inform personal stress management as well as interventions when necessary. In this thesis, I present StressAware, an application on the Amulet wearable platform to measure the stress levels of individuals continuously and in real time. The app implements a stress detection model, continuously streams heart rate data from a commercial heart-rate monitor such as a Zephyr and Polar H7, classifies the stress level of an individual, logs the stress level and then displays it as a graph on the screen. I developed a stress detection model using a Linear Support Vector Machine. I trained my classifiers using data from 3 sources: PhysioNet, a public database with various physiological data, a field study, where subjects went about their normal daily activities and a lab study in a controlled environment, where subjects were exposed to various stressors. I used 73 data segments of stress data obtained from PhysioNet, 120 data segments from the field study, and 14 data segments from the lab study. I extracted 14 heart rate and heart rate variability features. With 10-fold cross validation for Radial Basis Function (RBF) SVM, I obtained an accuracy of 94.5\% for the PhysioNet dataset and 100\% for the field study dataset. And for the lab study, I obtained an accuracy of 64.29\% with leave-one-out cross-validation. Testing the StressAware app revealed a projected battery life of up to 12 days before needing to recharge. Also, the usability feedback from subjects showed that the Amulet and Zephyr have a potential to be used by people for monitoring their stress levels. The results are promising, indicating that the app may be used for stress detection, and eventually for the development of stress-related intervention that could improve the health of individuals.}, } @TechReport{knowles:amulet-bt, author = {Anna J. Knowles}, title = {{Integrating Bluetooth Low Energy Peripherals with the Amulet}}, institution = {Dartmouth Computer Science}, year = 2016, month = {May}, number = {TR2016-807}, copyright = {the author}, address = {Hanover, NH}, URL = {https://www.cs.dartmouth.edu/~kotz/research/knowles-amulet-bt/index.html}, abstract = {The Amulet is a health monitor, similar in size and shape to a smartwatch but specifically designed to have a longer battery life and handle data securely. It is equipped with a Bluetooth Low Energy (BLE) radio in order to receive data from BLE-enabled sensors and transmit data to smartphones, but the full implementation of BLE communication on the Amulet is still a work in progress. This thesis describes architectural changes that improve the Amulet's ability to receive data from a variety of BLE-enabled sensors and make it easier for developers to integrate new BLE-enabled sensors with the Amulet by introducing support for connecting to multiple sensors at the same time, rewriting the radio code to be more generic, and exposing BLE functionality to the AmuletOS. We discuss the relevant parts of the AmuletOS and the BLE protocol as background, describe the current structure of BLE communications on the Amulet, and document the proposed changes to create a system for easily integrating new BLE-enabled sensors and handling connections to multiple sensors simultaneously.}, } @InProceedings{mm:amulet-poster, author = {Andr{\'{e}}s Molina-Markham and Ronald A. Peterson and Joseph Skinner and Ryan J. Halter and Jacob Sorber and David Kotz}, title = {{Poster: Enabling Computational Jewelry for mHealth Applications}}, booktitle = {{Proceedings of the International Conference on Mobile Systems, Applications, and Services (MobiSys)}}, year = 2014, month = {June}, pages = {374--375}, publisher = {ACM}, copyright = {ACM}, DOI = {10.1145/2594368.2601454}, URL = {https://www.cs.dartmouth.edu/~kotz/research/mm-amulet-poster/index.html}, abstract = {We are developing wearable devices as the foundation for a consistently present and highly available body-area mHealth network. Our vision is that a small device, such as a bracelet or pendant, will provide the availability and reliability properties essential for successful body-area mHealth networks. We call this class of device computational jewelry, and expect it will be the next frontier of mobile systems. We prototyped our first piece of computational jewelry, which we call Amulet, to enable our previously proposed vision. It runs applications that may collect sensor data from built-in sensors or from other devices, analyze and log the data, queue information for later upload, and interact with the wearer. Independent developers can develop applications that can be vetted and installed on an Amulet.}, } @InProceedings{molina-markham:wmmadd, author = {Andr{\'{e}}s Molina-Markham and Ronald Peterson and Joseph Skinner and Tianlong Yun and Bhargav Golla and Kevin Freeman and Travis Peters and Jacob Sorber and Ryan Halter and David Kotz}, title = {{Amulet: A secure architecture for mHealth applications for low-power wearable devices}}, booktitle = {{Proceedings of the Workshop on Mobile Medical Applications-- Design and Development (WMMADD)}}, year = 2014, month = {November}, pages = {16--21}, publisher = {ACM}, copyright = {ACM}, DOI = {10.1145/2676431.2676432}, URL = {https://www.cs.dartmouth.edu/~kotz/research/molina-markham-wmmadd/index.html}, abstract = {Interest in using mobile technologies for health-related applications (mHealth) has increased. However, none of the available mobile platforms provide the essential properties that are needed by these applications. An mHealth platform must be (i) secure; (ii) provide high availability; and (iii) allow for the deployment of multiple third-party mHealth applications that share access to an individual's devices and data. Smartphones may not be able to provide property (ii) because there are activities and situations in which an individual may not be able to carry them (e.g., while in a contact sport). A low-power wearable device can provide higher availability, remaining attached to the user during most activities. Furthermore, some mHealth applications require integrating multiple on-body or near-body devices, some owned by a single individual, but others shared with multiple individuals. In this paper, we propose a secure system architecture for a low-power bracelet that can run multiple applications and manage access to shared resources in a body-area mHealth network. The wearer can install a personalized mix of third-party applications to support the monitoring of multiple medical conditions or wellness goals, with strong security safeguards. Our preliminary implementation and evaluation supports the hypothesis that our approach allows for the implementation of a resource monitor on far less power than would be consumed by a mobile device running Linux or Android. Our preliminary experiments demonstrate that our secure architecture would enable applications to run for several weeks on a small wearable device without recharging.}, } @InProceedings{sorber:amulet, author = {Jacob Sorber and Minho Shin and Ronald Peterson and Cory Cornelius and Shrirang Mare and Aarathi Prasad and Zachary Marois and Emma Smithayer and David Kotz}, title = {{An Amulet for trustworthy wearable mHealth}}, booktitle = {{Proceedings of the Workshop on Mobile Computing Systems and Applications (HotMobile)}}, year = 2012, month = {February}, articleno = 7, numpages = 6, publisher = {ACM}, copyright = {ACM}, location = {San Diego, California}, DOI = {10.1145/2162081.2162092}, URL = {https://www.cs.dartmouth.edu/~kotz/research/sorber-amulet/index.html}, abstract = {Mobile technology has significant potential to help revolutionize personal wellness and the delivery of healthcare. Mobile phones, wearable sensors, and home-based tele-medicine devices can help caregivers and individuals themselves better monitor and manage their health. While the potential benefits of this ``mHealth'' technology include better health, more effective healthcare, and reduced cost, this technology also poses significant security and privacy challenges. In this paper we propose \emph{Amulet,} an mHealth architecture that provides strong security and privacy guarantees while remaining easy to use, and outline the research and engineering challenges required to realize the Amulet vision.}, }