Feasibility of Integrating Electromyography and Computer Vision for Occupational Safety during Tractor Ingress and Egress

Abstract

Entering and exiting a tractor requires strength, muscle coordination, and behaviors that can prevent injuries Research indicates between 15-20% of tractor-related injuries occur during ingress and egress (entry and exit, respectively) (Douphrate et al., 2009). Most agricultural producers know it is important to maintain three points of contact, to face toward the cab when climbing in and out, to have clean steps, and to wear anti-slip shoes to ensure safe ingress/egress. However, these steps are not always taken by producers in the field. Efforts are needed to improve safety of ingress and egress behaviors including learning more about human performance and capabilities. This paper will describe the application of a custom computer vision system to assess behaviors for tractor ingress and egress along with the physical performance. The objective of this study was to demonstrate the feasibility of integrating EMG and computer vision data to study biomechanics and behavior to assess tractor ingress and egress safety. Two participants had EMG sensors placed bilaterally. They completed a grip strength test using a hand dynamometer and then climbed into and out of the tractor. The extensor carpi radialis longus (ECRL) muscle activation on both sides demonstrated the highest activation as a percentage of the maximum activation. When climbing down, the participants’ shoulders retracted, causing the ECRL to have more activation than the ingress movements. However, during ingress the participants received more visual information by looking at their hands and rails, which could contribute to why the strategy they use is completely different. During egress the participant was observed to focus on their legs and the visual cues they received were in the form of looking down at their feet. The value of these different visual stimuli is vastly different and require further research to see how they contribute to overall movement strategies. This could lead participants to either leaning toward the cab or away from the cab and requiring different muscle activation in their upper body. It is anticipated that during egress the leg muscles could be more active than the arm muscles with even more activation during ingress due to working against gravity to climb into the cab. The computer vision algorithm was able categorize safety risk levels during the trials. The use of EMG combined with computer vision has the potential to describe movement patterns and behaviors that could impact ingress and egress safety. Further refinement and synchronization of these systems are needed to use this method for developing and testing targeted fall prevention interventions and to create user-centered ingress and egress design solutions.

Keywords: Fall Risk, Machine Learning, Strength, Capacity, Safety Behaviors

DOI: 10.54941/ahfe1005301