Abstract / truncated to 115 words (read the full abstract)

This thesis contributes to motion characterization through inertial and physiological signals captured by wearable devices and analyzed using signal processing and deep learning techniques. This research leverages the possibilities of motion analysis for three main applications: to know what physical activity a person is performing (Human Activity Recognition), to identify who is performing that motion (user identification) or know how the movement is being performed (motor anomaly detection). Most previous research has addressed human motion modeling using invasive sensors in contact with the user or intrusive sensors that modify the user’s behavior while performing an action (cameras or microphones). In this sense, wearable devices such as smartphones and smartwatches can collect motion signals from users ... toggle 8 keywords

motion modelling inertial signals physiological signals deep learning artificial intelligence human activity recognition identity supervision motor anomaly detection


Gil-Martín, Manuel
Universidad Politécnica de Madrid
Publication Year
Upload Date
Feb. 16, 2024

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