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Abstract:
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Activity patterns can be informative about a patient's health status. Traditionally, clinicians and medical researchers have used surveys to monitor patients' activity levels. These self-report data provide a useful firsthand account, but they can suffer from bias. Smartphones offer an opportunity to address these challenges. The smartphone has built-in sensors that collect data objectively, unobtrusively, and continuously. Due to their widespread adoption, smartphones are also accessible to most of the population. Currently, a main challenge in smartphone-based activity recognition is in extracting information optimally from multiple sensors to identify the unique features of different activities. In our study, we analyze data collected by two sensors in the phone, the accelerometer and gyroscope, which measure the phone's acceleration and angular velocity, respectively. We propose an extension to the ``movelet method'' that jointly incorporates smartphone accelerometer and gyroscope data. We apply this method to a data set that we collected, and compare the joint-sensor results to those from using each sensor alone.
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