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Article

HARTH: A Human Activity Recognition Dataset for Machine Learning

1
Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, 7034 Trondheim, Norway
2
Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway
3
Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway
4
Department of Sport, Food and Natural Sciences, Faculty of Education, Arts and Sports, Western Norway University of Applied Sciences, 6851 Sogndal, Norway
*
Author to whom correspondence should be addressed.
Academic Editor: Kevin Bell
Sensors 2021, 21(23), 7853; https://doi.org/10.3390/s21237853
Received: 30 September 2021 / Revised: 17 November 2021 / Accepted: 22 November 2021 / Published: 25 November 2021
(This article belongs to the Special Issue Advances and Application of Human Movement Sensors)
Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this work. First, we present the publicly available Human Activity Recognition Trondheim dataset (HARTH). Twenty-two participants were recorded for 90 to 120 min during their regular working hours using two three-axial accelerometers, attached to the thigh and lower back, and a chest-mounted camera. Experts annotated the data independently using the camera’s video signal and achieved high inter-rater agreement (Fleiss’ Kappa =0.96). They labeled twelve activities. The second contribution of this paper is the training of seven different baseline machine learning models for HAR on our dataset. We used a support vector machine, k-nearest neighbor, random forest, extreme gradient boost, convolutional neural network, bidirectional long short-term memory, and convolutional neural network with multi-resolution blocks. The support vector machine achieved the best results with an F1-score of 0.81 (standard deviation: ±0.18), recall of 0.85±0.13, and precision of 0.79±0.22 in a leave-one-subject-out cross-validation. Our highly professional recordings and annotations provide a promising benchmark dataset for researchers to develop innovative machine learning approaches for precise HAR in free living. View Full-Text
Keywords: physical activity behavior; human activity recognition; public dataset; benchmark; machine learning; deep learning; accelerometer physical activity behavior; human activity recognition; public dataset; benchmark; machine learning; deep learning; accelerometer
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MDPI and ACS Style

Logacjov, A.; Bach, K.; Kongsvold, A.; Bårdstu, H.B.; Mork, P.J. HARTH: A Human Activity Recognition Dataset for Machine Learning. Sensors 2021, 21, 7853. https://doi.org/10.3390/s21237853

AMA Style

Logacjov A, Bach K, Kongsvold A, Bårdstu HB, Mork PJ. HARTH: A Human Activity Recognition Dataset for Machine Learning. Sensors. 2021; 21(23):7853. https://doi.org/10.3390/s21237853

Chicago/Turabian Style

Logacjov, Aleksej, Kerstin Bach, Atle Kongsvold, Hilde B. Bårdstu, and Paul J. Mork 2021. "HARTH: A Human Activity Recognition Dataset for Machine Learning" Sensors 21, no. 23: 7853. https://doi.org/10.3390/s21237853

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