Productivity Measurement with the Use of Pose Classification and Machine Learning with Fuzzy Approximate Reasoning
Abstract
Object detection refers to investigating the relationships between images or videos and detected objects to improve system utilization and make better decisions. Productivity measurement plays a key role in assessing operational efficiency across different industries. However, capturing the workers’ working status can be resource-intensive and constrained by a limited sample size if the sampling is conducted manually. While the use of object detection approaches has provided a shortcut for collecting image samples, classifying human poses involves training pose estimation models that may often require a substantial effort for annotating the images. In this study, a systematic approach that integrates pose estimation techniques, fuzzy-set theory, and machine learning algorithms has been proposed at an affordable level of computational resources. The Random Forests algorithm has been explored for handling classification tasks, while fuzzy approximation has also been applied to capture the imprecision associated with human poses, enhancing robustness to variability and accounting for inherent uncertainty. Decision-makers can utilize the proposed approach without the need for high computational resources or extensive data collection efforts, making it suitable for deployment in various workplace environments.
