Assessing blood-pressure measurement in tablet-based mHealth apps
[murthy:bp]Rima Murthy and David Kotz. Assessing blood-pressure measurement in tablet-based mHealth apps. Proceedings of the Workshop on Networked Healthcare Technology (NetHealth), pages 1–5. IEEE, January 2014. doi:10.1109/COMSNETS.2014.6734920. ©Copyright IEEE.
We propose a new method to record contextual information associated with a blood-pressure reading using a tablet’s touchscreen and accelerometer. This contextual information can be used to verify that a patient’s lower arm remained well-supported and stationary during her blood-pressure measurement. We found that a binary support vector machine classifier could be used to distinguish different types of lower-arm movements from stationary arms with 90% accuracy overall. Predetermined thresholds for the accelerometer readings suffice to determine whether the tablet, and therefore the arm that rested on it, remained supported. Together, these two methods can allow mHealth applications to guide untrained patients (or health workers) in measuring blood pressure correctly.
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Keywords: [mhealth] [sensors]
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