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This study proposes a computer vision framework to monitor worker joint motion from task videos. The framework focuses on landmark features, particularly those associated with participants’ upper and lower limbs, to extract spatial joint movements. By utilizing the Hotelling T-squared statistic, multivariate joint motion distributions were monitored. The application of control chart techniques involved two phases: Phase I (offline) and Phase II (online) monitoring. For implementation, task videos were strategically partitioned into two segments. The first segment was designated for offline training, allowing for the establishment of baseline patterns. The second was allocated for online monitoring, enabling the real-time evaluation of worker demand levels during operational activities. The correlation between the amount of motion and task perception aligns with participants’ ratings from the perception survey, validating the framework’s effectiveness. Understanding how workers interact with products and equipment allows designers to create tools that are easier and more comfortable to use.
Iyer et al. (Tue,) studied this question.