Abstract
This paper introduces a human posture tracking platform to identify the human postures of sitting, standing or lying down, based on a smartwatch. This work develops such a system as a proof-of-concept study to investigate a smartwatch’s ability to be used in future remote health monitoring systems and applications. This work validates the smartwatches’ ability to track the posture of users accurately in a laboratory setting while reducing the sampling rate to potentially improve battery life, the first steps in verifying that such a system would work in future clinical settings. The algorithm developed classifies the transitions between three posture states of sitting, standing and lying down, by identifying these transition movements, as well as other movements that might be mistaken for these transitions. The system is trained and developed on a Samsung Galaxy Gear smartwatch, and the algorithm was validated through a leave-one-subject-out cross-validation of 20 subjects. The system can identify the appropriate transitions at only 10 Hz with an F-score of 0.930, indicating its ability to effectively replace smart phones, if needed.
Highlights
Wearable and mobile sensors are increasingly prevalent, with studies showing users are within proximity of their smartphones almost 90% of the time [1]
This paper aims to address similar accuracy presented by these works on posture tracking using a smar twatch platform
This work introduced a smartwatch-based system to assist in tracking the posture of users wearing a wrist-worn platform instead of a hip-worn platform
Summary
Wearable and mobile sensors are increasingly prevalent, with studies showing users are within proximity of their smartphones almost 90% of the time [1]. These phones have impressive sensing capabilities, ranging from remote health monitoring [2,3] to ubiquitous life-logging [4]. This work investigates whether the smartwatch can effectively track user activity and posture without the aide of a smartphone, to potentially serve as the base platform for a remote health monitoring system for oncology patients
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