Measuring elemental time and duty cycle using automated video processing

Abstract

A marker-less 2D video algorithm measured hand kinematics (location, velocity and acceleration) in a paced repetitive laboratory task for varying hand activity levels (HAL). The decision tree (DT) algorithm identified the trajectory of the hand using spatiotemporal relationships during the exertion and rest states. The feature vector training (FVT) method utilised the k-nearest neighbourhood classifier, trained using a set of samples or the first cycle. The average duty cycle (DC) error using the DT algorithm was 2.7%. The FVT algorithm had an average 3.3% error when trained using the first cycle sample of each repetitive task, and had a 2.8% average error when trained using several representative repetitive cycles. Error for HAL was 0.1 for both algorithms, which was considered negligible. Elemental time, stratified by task and subject, were not statistically different from ground truth (p < 0.05). Both algorithms performed well for automatically measuring elapsed time, DC and HAL.

Practitioner Summary: A completely automated approach for measuring elapsed time and DC was developed using marker-less video tracking and the tracked kinematic record. Such an approach is automatic, repeatable, objective and unobtrusive, and is suitable for evaluating repetitive exertions, muscle fatigue and manual tasks.

Keywords:

• Repetitive motion
• work-related musculoskeletal disorders
• exposure assessment
• time and motion study

Acknowledgement

The views expressed do not necessarily reflect the official policies of the Department of Health and Human Services, nor does mention of trade names, commercial practices, or organisations imply endorsement by the U.S. Government. The authors would like to acknowledge David Hintz for assistance in designing and fabricating with the laboratory apparatus. We would also like to thank Eric Chen and David Azari for their assistance in running the laboratory studies.