Classifying tasks performed by electrical line workers using a wrist-worn sensor: A data analytic approach

Electrical line workers (ELWs) experience harsh environments, characterized by long shifts, remote operations, and potentially risky tasks. Wearables present an opportunity for unobtrusive monitoring of productivity and safety. A prerequisite to monitoring is the automated identification of the task...

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Veröffentlicht in:	PloS one 2022-12, Vol.17 (12), p.e0261765
Hauptverfasser:	Ragani Lamooki, Saeb, Hajifar, Sahand, Hannan, Jacqueline, Sun, Hongyue, Megahed, Fadel, Cavuoto, Lora
Format:	Artikel
Sprache:	eng
Schlagworte:	Accelerometers Accelerometry Accuracy Activities of Daily Living Algorithms Biology and Life Sciences Classification Computer and Information Sciences Data Science Disease control Economic development Electric power Engineering and Technology Evaluation Forecasts and trends Harsh environments Health risks Human activity recognition Human influences Human performance Humans Infrastructure (Economics) Internet of Things Kinematics Medicine and Health Sciences Mitigation Monitoring Occupational safety Research and Analysis Methods Risk assessment Risk reduction Sensors Smartphones Social Sciences Task complexity United States Workers Wrist Wrist Joint
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