Forestry Workforce Location- and Wearable-based Activity Recognition to Quantify on-the Job Digital Health and Safety Metrics

Logging is among the most dangerous professions in the United States. Manual felling of timber with chainsaws and setting of cable log chokers accounted for 47% of injuries in Idaho between 2011-2014. Building on a recent PNASH pilot project, a library of wearable- and location-based human activity recognition (HAR) models will be developed and coded into a smartwatch app prototype to enhance the safety and efficiency of forestry work in Idaho, Oregon, and Washington through increased situational awareness (SA) among workers on remote cable logging operations.  

This project will: 

  1. develop HAR models needed to classify logging work activities in real-time using smartwatches;  

  1. code and evaluate those models using a smartwatch app prototype; and  

  2. evaluate the smartwatch app jointly with a location-sharing system for active forest operations to provide both location- and smartwatch HAR-based information for improved SA to reduce logging injuries.  

HAR models will be developed and evaluated for three core job tasks on cable logging operations: manual felling of timber, manual setting of chokers during yarding of trees, and heavy equipment operation. Project deliverables include a prototype smartwatch HAR app, four peer-reviewed publications, an outreach publication, training workshops for loggers and forest managers, and a dedicated social media account. We expect outcomes of this project to include a reduction in the risk of work-related illnesses and traumatic injuries among forestry workers. 

Principal Investigators: Robert Keefe, Associate Professor and Director, University of Idaho Experimental Forest, and, Eloise Zimbelman, Postdoctoral Scholar, PNASH Center and University of Idaho 

NIOSH Funding Period 2022-2027