Towards a multimodal human activity dataset for healthcare

Towards a multimodal human activity dataset for healthcare

Human activity recognition (HAR) based on wearable devices has become a hot topic due to the wide adoption of smartphones and smart bands. In this paper, we propose a new dataset, MMC-PCL-Activity, for wearable device-based HAR. It contains data of accelerometers, gyroscopes, heart rates, steps, GPS...

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Veröffentlicht in:Multimedia systems 2023-02, Vol.29 (1), p.1-13
Hauptverfasser: Hu, Menghao, Luo, Mingxuan, Huang, Menghua, Meng, Wenhua, Xiong, Baochen, Yang, Xiaoshan, Sang, Jitao
Format: Artikel
Sprache:eng
Schlagworte:
Accelerometers
Annotations
Applications programs
Computer Communication Networks
Computer Graphics
Computer Science
Cryptology
Data Storage Representation
Datasets
Heart rate
Human activity recognition
Mental health
Meteorological data
Mobile computing
Multimedia Information Systems
Operating Systems
Regular Paper
Smartphones
Wearable technology
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Beschreibung
Zusammenfassung:Human activity recognition (HAR) based on wearable devices has become a hot topic due to the wide adoption of smartphones and smart bands. In this paper, we propose a new dataset, MMC-PCL-Activity, for wearable device-based HAR. It contains data of accelerometers, gyroscopes, heart rates, steps, GPS, weather information, mobile APP usage, and images collected from 14 participants performing 16 different types of daily activities. Besides the activity annotations, labels of physical health status and mental health status are also provided. We demonstrate the importance of multimodal fusion in activity recognition and provide baselines for more researchers using this dataset.
ISSN:0942-4962
1432-1882
DOI:10.1007/s00530-021-00875-6