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Activity Detection using Biosignals from Wearable Devices

Tong Shan, Dushyanth Sirivolu, Fulya Tastan, Philip Barron, Larsen Linov, Ming Li


Our goal is to study the biosignal pattern of everyday activity like walking, running, lifting chairs, etc, and creates machine learning models to recognize human daily activities from biosignals recorded by wearable devices. These signals includes electrocardiography (ECG), electrodermal activity (EDA), and photoplethysmography (PPG), electromyography (EMG), wrist temperature (TEMP) and chest and wrist actigraphy (ACC). The algorithms can be used in detecting user's daily activities and monitoring user's health condition.

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