Development and validation of smartwatch-based activity recognition models for rigging crew workers on cable logging operations.

Details

• Personal Author:
  Keefe RF ; Zimbelman EG
• Description:
  Analysis of high-resolution inertial sensor and global navigation satellite system (GNSS) data collected by mobile and wearable devices is a relatively new methodology in forestry and safety research that provides opportunities for modeling work activities in greater detail than traditional time study analysis. The objective of this study was to evaluate whether smartwatch-based activity recognition models could quantify the activities of rigging crew workers setting and disconnecting log chokers on cable logging operations. Four productive cycle elements (travel to log, set choker, travel away, clear) were timed for choker setters and four productive cycle elements (travel to log, unhook, travel away, clear) were timed for chasers working at five logging sites in North Idaho. Each worker wore a smartwatch that recorded accelerometer data at 25 Hz. Random forest machine learning was used to develop predictive models that classified the different cycle elements based on features extracted from the smartwatch acceleration data using 15 sliding window sizes (1 to 15 s) and five window overlap levels (0%, 25%, 50%, 75%, and 90%). Models were compared using multiclass area under the Receiver Operating Characteristic (ROC) curve, or AUC. The best choker setter model was created using a 3-s window with 90% overlap and had sensitivity values ranging from 76.95% to 83.59% and precision values ranging from 41.42% to 97.08%. The best chaser model was created using a 1-s window with 90% overlap and had sensitivity values ranging from 71.95% to 82.75% and precision values ranging from 14.74% to 99.16%. These results have demonstrated the feasibility of quantifying forestry work activities using smartwatch-based activity recognition models, a basic step needed to develop real-time safety notifications associated with high-risk job functions and to advance subsequent, comparative analysis of health and safety metrics across stand, site, and work conditions. [Description provided by NIOSH]
• Subjects:
  Forestry Occupational Health Safety Safety Management
• Keywords:
  Wearable technologies; Forestry workers; Emerging technology; Monitoring systems; Safety monitoring; Work analysis; Logging workers
• ISSN:
  1932-6203
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  Idaho ; OSHA Region 10 ; Washington
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  16
• Issue:
  5
• NIOSHTIC Number:
  nn:20065417
• Citation:
  PLoS One 2021 May; 16(5):e0250624
• Contact Point Address:
  Eloise G. Zimbelman, Department of Forest, Rangeland and Fire Sciences, University of Idaho, Moscow, ID
• Email:
  eloisez@uidaho.edu
• Federal Fiscal Year:
  2021
• NORA Priority Area:
  Agriculture, Forestry and Fishing
• Performing Organization:
  University of Washington
• Peer Reviewed:
  True
• Start Date:
  20010930
• Source Full Name:
  PLoS One
• End Date:
  20270929
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:dfe20674e770c3f1d0c725e3913084bc83a272246cb229eadef08734655a113719f1d996712edfd971daea9102cb80001c630b8a896653c8954d1a606f38b144
• Download URL:
  https://stacks.cdc.gov/view/cdc/248853/cdc_248853_DS1.pdf
• File Type:
  [PDF - 3.60 MB ]