This project is no longer active; this page is no longer updated.
Related website: [Amulet-project.org]
Related projects: [Amanuensis], [Amulet], [Auracle], [SIMBA], [THaW], [TISH]
Related keywords: [iot], [mhealth], [patent], [privacy], [security], [sensors], [wearable]
In the Amulet project we developed a custom wrist-worn computing platform for mobile health (mHealth). Amulet is (1) a bracelet 'hub' for a body-area mHealth network; (2) a secure, multi-application mHealth platform; (3) always on - and always with you; (4) long lasting, with battery life of one week to one month; (5) discreet in communicating with its wearer; (6) able to support multiple apps that monitor stress, physical activity, and exercise in free-living conditions; (7) open-hardware and open-source. The original concept was described in a 2012 HotMobile paper [sorber:amulet], and two retrospective papers provide an overview of the whole project in 2019 [boateng:experience, kotz:amulet19]. The interactive tool to predict and tune energy consumption is described further in Travis Peters' dissertation [peters:thesis].
Our primary contribution was the development of the Amulet platform itself [hester:amulet], 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. Our prototype had battery lifetime lasting weeks or even months, depending on the application, and our interactive resource-profiling tool predicted battery lifetime within 6-10% of the measured lifetime.
We also made several contributions to security, including innovative ways for memory protection on low-capability microcontrollers like the MSP430 used in Amulet [hardin:mpu, hardin:mobisys17]. This work appears again as a chapter in Hardin's thesis [hardin:thesis].
We further developed methods for cryptographic transfer of data from a low-power wearable (like Amulet) to a smartphone [harmon:thesis], and then from a smartphone into a secure cloud-based mHealth data storage system... which then allows the data contributor to control which data consumer(s) can retrieve certain slices of data from their mHealth streams [greene:sharehealth, greene:thesis].
We also developed several algorithms, applications, and studies related to the measurement of stress or the monitoring of physical activity [mishra:commodity, boateng:stepcount, boateng:geriactive, boateng:activityaware, boateng:stressaware, boateng:msthesis, boateng:stressaware-thesis]. In the culmination of this work, we showed that, using and off-the-shelf heart-rate sensor, 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 [mishra:jcommodity]. For follow-on work, see the SIMBA project.
In collaboration with engineers, we assisted with the development of a custom, low-power, wrist-worn sensor for electrodermal activity (EDA), which can be helpful in measuring stress [pope:eda-bsn].
In collaboration with a physician and students at the medical school, we also demonstrated the potential for Amulet (and devices like it) to be used for remote- and home-monitoring of physical activity among older adults, with the aim of assisting them to retain physical capabilities sufficient for activities of daily living [batsis:rural, batsis:mowi, batsis:amulet-use, batsis:change, batsis:feasibility, batsis:usability, batsis:weight-loss, batsis:barriers, rauch:wtp, petersen:design].
We also assisted in the development of a customized handle for an exercise resistance band -- typified by those from brand Theraband -- to enable clinical and research teams to measure a participant's use of the exercise band. The handle measures force induced on the handle by the band, and algorithms extract the number of repetitions [seo:theraband, peterson:chase, batsis:development].
For more information about the Amulet project, and a broader description of its contributions and publications (not just those including David Kotz and his students), see the Amulet website.
Amulet is freely available for personal, educational, and research use, open-hardware and open-source.
Some Amulet technology is covered by two patents: [kotz:patent9936877, kotz:patent9595187].
Amulet involved many students, staff, and faculty, primarily at Dartmouth and Clemson University.
Core researchers:
John Batsis,
George Boateng,
Kelly Caine,
Eric Chen,
Kevin Freeman,
Emily Greene,
Bhargav Golla,
Ryan Halter,
Taylor Hardin,
David Harmon,
Josiah Hester,
Hilary Johnson,
Anna Knowles,
David Kotz,
Sarah Lord,
Byron Lowens,
Zachary Marois,
Varun Mishra,
Andrés Molina-Markham,
Vivian Genaro Motti,
Emma Oberstein,
Travis Peters,
Curtis Petersen,
Ronald Peterson,
Gunnar Pope,
Patrick Proctor,
Sougata Sen,
Lillian Seo,
Joseph Skinner,
Jacob Sorber,
Kevin Storer,
Emily Wechsler,
and
Tianlong Yun.
Collaborators:
Christina Aquila,
Rima Al-Nimr,
Stephen Bartels,
Hang Cai,
Elizabeth Carpenter-Song,
Matthew Clark,
Summer Cook,
Cory Cornelius,
Kevin Curtis,
Rachel Dokko,
Karen Fortuna,
Diane Gilbert-Diamond,
Tyler Gooding,
Ann Haedrich,
Steven Hearndon,
Stephanie Lewia,
Francisco Lopez-Jimenez,
Jasmine Mai,
Todd Mackenzie,
Shrirang Mare,
Auden McClure,
John Naslund,
Dawna Pidgeon,
Aarathi Prasad,
Meredith Roderka,
Sivan Rotenberg,
Richard Rothstein,
Ryan Scott,
Diane Sette,
Minho Shin,
Emma Smithayer,
Courtney Stevens,
Aaron Weintraub,
K.C. Wright,
and
Alexandra Zagaria.
This research program was primarily supported by the US National Science Foundation under award numbers CNS-1314281, CNS-1314342, CNS-1619970, and CNS-1619950. Some preliminary work was supported by the US National Science Foundation under award number 0910842 and by the US Department of Health and Human Services (SHARP program) under award number 90TR0003-01. Research reported by author John Batsis was supported in part by the US National Institute On Aging of the National Institutes of Health under Award Number K23AG051681.
The views and conclusions contained on this site are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the sponsors. Any mention of specific companies or products does not imply any endorsement by the authors or by the sponsors.
The Amulet logo was designed by the students of the DALI lab at Dartmouth. We are grateful for their creativity and assistance!
This list includes only those including David Kotz as co-author or thesis advisor. For a complete list of Amulet papers, see the Amulet website.
[The list below is also available in BibTeX]
Papers are listed in reverse-chronological order;
click an entry to pop up the abstract.
For full information and pdf, please click Details link.
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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.
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.
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.
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.
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.
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.
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.
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.
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 ≥65 years with a body mass index (BMI) ≥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.
Results: Mean age was 72.9±3.9 years (82% female). Baseline anthropometric measures of weight, BMI, and waist circumference were 97.8±16.3 kg, 36.5±5.2 kg/m2, and 115.5±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±19.3% of intervention days. In completers, mean weight loss was 4.6±3.5 kg or 4.7±3.5% (p<0.001). Physical function measures of 30-s sit-to-stand repetitions increased from 13.5±5.7 to 16.7±5.9 (p<0.001), 6-min walk improved by 42.0±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±4.7 points, p<0.001).
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.
Methods: A 12-week pilot in 28 older rural adults with obesity (body mass index [BMI] ≥ 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.
Results: Mean age was 72.9 ± 5.3 years (82% female). Baseline BMI was 37.1 kg/m2, and waist circumference was 120.0 ± 33.0 cm. Mean weight loss (pre/post) was 4.6 ± 3.2 kg (4.9 ± 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 ± 41.2 m (p < .001); gait speed improved: 0.10 ± 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 ± 7.1 points, p = .003), as did Patient-Reported Outcome Measurement Information Systems mental and physical health scores (5.0 ± 5.7 and 4.4 ± 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).
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.
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.
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.
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 (ß=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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Conclusions: Older rural adults with obesity expressed excitement about the use of mHealth technologies to improve their health, yet barriers to implementation exist.
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.
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.
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.