  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.
