Time Series Representation using TS2Vec on Smartwatch Sensor Data for Fatigue Estimation
Authors: Adria Mallol - Ragolta, Michail Maniadakis, George Papadopoulos, Bjoern Schuller
Abstract: This work investigates the use of TS2Vec time series representations in an end-to-end approach to detect the fatigue levels perceived by workers of the transportation and logistics industry from the analysis of the accelerometer and the heart rate measurements sensed using a Garmin Vivoactive 3 device. The experiments are conducted using the dataset collected during a pre-pilot study with a total of 1 h 22 min 20 sec of data available. The results obtained support the use of TS2Vec representations for the task at hand, as the binary model trained using this approach and exploiting the heart rate modality obtains the best performance with an Unweighted Average Recall of 67.1 %.
Keywords: Fatigue estimation, AI, ubiquitous sensing
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