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Using Active Learning for Activity Recognition on Smartwatches


Advancements in wearable technologies have equipped smartwatches with various sensors. Motion and direction sensors, namely accelerometers, gyroscopes, and magnetometers, have broad applications in human activity recognition. While many activity recognition algorithms have been proposed in recent years, most studies emphasize the use of inertial sensors in smartphone devices or other body-worn sensors. By comparison, very few works have evaluated the application of smartwatches for activity monitoring applications. In this study, we present a system to detect five daily activities using smartwatches. Our system identifies activities with 91.9% accuracy based on over 540 minutes of data collected from twelve subjects. We also demonstrate that the opportunity to deploy active learning for activity recognition can be a significant advantage of smartwatches over other devices. By collecting personal data from subjects online, active learning improves the accuracy of classifier predictions, while lowering variance between different users.

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