Pilot study on strategies in sensor placement for robust hand/wrist gesture classification based on movement related changes in forearm volume

Force Myography (FMG) is novel method of tracking functional motor activity using volumetric changes associated with muscle function. With comparable accuracy and multiple advantages over traditional methods of functional motor activity tracking, FMG has made leaps and bounds in terms of applications in human-machine interfaces and healthcare devices. As a field that is rapidly gaining popularity in health innovation, the aim of this paper is to contribute to our understanding of the nature FMG methods and establish it as a robust and reliable technique. The main point of exploration for this study is the impact of sensor placement and spatial coverage on FMG methods. Five participants were invited to perform a series of isolated wrist motions and hand gestures while wearing custom built FMG devices. Linear Discriminant Analysis (LDA) machine learning models were developed using 80% of the data for training and 20% for testing. Overall, the accuracy of the LDA models ranged from 75% to 100% across all subjects and dimensions of FMG data. The model accuracy improved when increasing the spatial coverage from 1 FMG band to 2, but it did not increase further with additions. The results also showed that the improved accuracy offered by a large spatial coverage of FMG data can be approximated by lower spatial coverage if sensors were place in an optimal location. This location was indicated to be midway between the wrist and the collective muscle bellies of intrinsic forearm muscles. The knowledge generated from this work aims serve as a guide towards the development of portable FMG based technology for widespread deployment in the general population. The hope is that the long-term benefits of continued FMG research will address issues in healthcare associated with disparities in access to medical technologies.