Abstract
This paper describes the machine learning (ML) method Head-AR, which achieved the highest performance in a competition with 11 other algorithms and won the Emteq Activity Recognition challenge. The goal of the challenge was to recognize eight activities of daily life from a device mounted on the head, which provided data from a 3-axis IMU: accelerometer, gyroscope, and magnetometer. The challenge dataset was collected by four subjects, of which one subject was used as a test for the challenge evaluation. The method processes the stream of sensors data and recognizes one of the eight activities every two seconds. The method is based on weighted ensemble learning, which combines three models: (i) a dynamic time warping classification model, which analyzes raw accelerometer data; (ii) a classification model that uses expert features; (iii) and a classification model that uses features selected by a feature selection algorithm. To compute the final output, the predictions of the three models are combined using a novel weighing scheme. The method achieved an F1-score of 61.25% on the competition’s evaluation.
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We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. The authors declare that they have no conflict of interest.
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Gjoreski, H. et al. (2021). Head-AR: Human Activity Recognition with Head-Mounted IMU Using Weighted Ensemble Learning. In: Ahad, M.A.R., Inoue, S., Roggen, D., Fujinami, K. (eds) Activity and Behavior Computing. Smart Innovation, Systems and Technologies, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-15-8944-7_10
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