Prediction Method for Large Grasping Force using Single-Channel Surface Electromyography: A Pilot Study
DOI:
https://doi.org/10.14738/aivp.106.13526Keywords:
Grasping force, Surface electromyography, Occupational health, Machine LearningAbstract
Frequent grasping with a large force in the workplace leads to work-related musculoskeletal disorders (WMSDs). Thus, the frequency and force of grasping in each worker should be monitored for preventing these WMSDs. The frequency of grasping can be counted by observation and existing methods using vision-based devices or wearable sensors. Grasping force measurement generally requires force sensors between the hand and objects. Almost force sensors are limited to the surface and shape of the object in measurement; thus, measurement method without force sensors is necessary to monitor grasping force in various workplaces. Previous studies developed measurement methods for grasping force using electromyography (EMG). However, almost existing methods require multiple-channels EMG for grasping force measurement. In addition, many methods were not applied to large grasping forces. Therefore, the objective of this study is to develop the prediction method for large grasping force using single-channel EMG. The proposed method predicts grasping force by machine learning and features calculated from single channel surface EMG (sEMG) signal on flexor digitorum superficialis (FDS) muscle. As a pilot study, the proposed method was tested by sEMG data of different large grasping forces (5 to 60 kgf). The results showed that the correlation between the actual and predicted grasping force was greater than 0.8. These results indicate that the proposed method could measure large grasping force by only single-channel sEMG.
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