Classifying Tasks Performed by Electrical Line Workers Using a Wrist-Worn Sensor: A Data Analytic Approach

Details

• Personal Author:
  Cavuoto L ; Hajifar S ; Hannan J ; Lamooki SR ; Megahed F ; Sun H
• Description:
  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 tasks being performed. Human activity recognition has been widely used for classification for activities of daily living. However, the literature is limited for electrical line maintenance/repair tasks due to task variety and complexity. We investigated how features can be engineered from a single wrist-worn accelerometer for the purpose of classifying ELW tasks. Specifically, three classifiers were investigated across three feature sets (time, frequency, and time-frequency) and two window lengths (4 and 10 seconds) to identify ten common ELW tasks. Based on data from 37 participants in a lab environment, two application scenarios were evaluated: (a) intra-subject, where individualized models were trained and deployed for each worker; and (b) inter-subject, where data was pooled to train a general model that can be deployed for new workers. Accuracies ≥ 93% were achieved for both scenarios, and increased to ≥96% with 10-second windows. Overall and class-specific feature importance were computed, and the impact of those features on the obtained predictions were explained. This work will contribute to the future risk mitigation of ELWs using wearables. [Description provided by NIOSH]
• Subjects:
  Electricity Occupational Health Safety Safety Management
• Keywords:
  Electrical Workers Long Work Hours Monitoring Systems Risk Assessment Safety Monitoring Sensors Wearable Technologies
• ISSN:
  1932-6203
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  New York ; Ohio ; OSHA Region 2 ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  17
• Issue:
  12
• NIOSHTIC Number:
  nn:20067784
• Citation:
  PLoS One 2022 Dec; 17(12):e0261765
• Contact Point Address:
  Lora Cavuoto, Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, United States of America
• Email:
  loracavu@buffalo.edu
• Federal Fiscal Year:
  2023
• Performing Organization:
  State University of New York at Buffalo
• Peer Reviewed:
  True
• Start Date:
  20200930
• Source Full Name:
  PLoS One
• End Date:
  20220929
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:570cab5178008cbbc38f62b94daa2e250c87268190a5a592066c57e1dd78c106f043be88d084ae38e831e9f78fba82081872a3a743a32ee8a5e4e81724ec010a
• Download URL:
  https://stacks.cdc.gov/view/cdc/230040/cdc_230040_DS1.pdf
• File Type:
  [PDF - 2.79 MB ]