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SeismoTracker: Upgrade any smart Wearable to enable a sensing of heart rate, respiration rate, and microvibrations

 
: Haescher, Marian; Matthies, Denys J.C.; Trimpop, John; Urban, Bodo

:

Kaye, J. ; Association for Computing Machinery -ACM-:
CHI EA '16, Proceedings of the 2016 CHI Conference. Extended Abstracts on Human Factors in Computing Systems : Santa Clara, California, USA, May 07 - 12, 2016
New York: ACM, 2016
ISBN: 978-1-4503-4082-3
pp.2209-2216
Conference on Human Factors in Computing Systems (CHI) <34, 2016, San Jose/Calif.>
English
Conference Paper
Fraunhofer IGD, Institutsteil Rostock ()
Fraunhofer IGD ()
Guiding Theme: Individual Health; Research Area: Human computer interaction (HCI); Human-computer interaction (HCI); life and medical science; personal health; smart watches

Abstract
In this paper we present a method to enable any smart Wearable to sense vital data in resting states. These resting states (e.g. sleeping, sitting calmly, etc.) imply the presence of low-amplitude body-motions. Our approach relies on seismocardiography (SCG), which only requires a built-in accelerometer. Compared to commonly applied technologies, such as photoplethysmography (PPG), our approach is not only tracking heart rate (HR), but also respiration rate (RR), and microvibrations (MV) of the muscles, while being also computational inexpensive. In addition, we can calculate several other parameters, such as HR variability and RR variability. Our extracted vital parameters match with the vital data gathered from clinical state-of-the art technology. These data allow us to gain an impression on the user's activity, quality of sleep, arousal and stress level over the whole day, week, month, or year. Moreover, we can detect whether a device is actually worn or doffed, which is crucial when connecting such data with health services. We implemented our method on two current smartwatches: a Simvalley AW420 RX as well as on a LG G Watch R and recorded user data for several months. A web platform enables to keep track of one's data.

: http://publica.fraunhofer.de/documents/N-445136.html