Covfefe: A Computer Vision Approach For Estimating Force Exertion

TL;DR

This paper presents a non-intrusive, scalable method using face videos and PPG signals to classify muscle force exertion levels, aiming to prevent injuries and improve worker safety.

Contribution

It introduces a novel approach combining face video analysis and PPG signals for non-intrusive force exertion classification, achieving high accuracy and robustness.

Findings

90% accuracy in classifying high effort levels

81.7% accuracy when combining PPG signals

Robust to talking during data collection

Abstract

Cumulative exposure to repetitive and forceful activities may lead to musculoskeletal injuries which not only reduce workers' efficiency and productivity, but also affect their quality of life. Thus, widely accessible techniques for reliable detection of unsafe muscle force exertion levels for human activity is necessary for their well-being. However, measurement of force exertion levels is challenging and the existing techniques pose a great challenge as they are either intrusive, interfere with human-machine interface, and/or subjective in the nature, thus are not scalable for all workers. In this work, we use face videos and the photoplethysmography (PPG) signals to classify force exertion levels of 0\%, 50\%, and 100\% (representing rest, moderate effort, and high effort), thus providing a non-intrusive and scalable approach. Efficient feature extraction approaches have been investigated, including standard deviation of the movement of different landmarks of the face, distances between peaks and troughs in the PPG signals. We note that the PPG signals can be obtained from the face videos, thus giving an efficient classification algorithm for the force exertion levels using face videos. Based on the data collected from 20 subjects, features extracted from the face videos give 90\% accuracy in classification among the 100\% and the combination of 0\% and 50\% datasets. Further combining the PPG signals provide 81.7\% accuracy. The approach is also shown to be robust to the correctly identify force level when the person is talking, even though such datasets are not included in the training.