Counting Grasping Action Using Force Myography: An Exploratory Study With Healthy Individuals

Abstract

BackgroundFunctional arm movements generally require grasping an object. The possibility of detecting and counting the action of grasping is believed to be of importance for individual with motor function deficits of the arm, as it could be an indication of the number of the functional arm movements performed by the individuals during rehabilitation. In this exploratory work, the feasibility of using armbands recording radial displacements of forearm muscles and tendons (ie, force myography, FMG) to estimate hand grasping with healthy individuals was investigated. In contrast to previous studies, this exploratory study investigates the feasibility of (1) detecting grasping when the participants move their arms, which could introduce large artifacts to the point of potentially preventing the practical use of the proposed technology, and (2) counting grasping during arm-reaching tasks.ObjectiveThe aim of this study was to determine the usefulness of FMG in the detection of functional arm movements. The use of FMG straps placed on the forearm is proposed for counting the number of grasping actions in the presence of arm movements.MethodsTen healthy volunteers participated in this study to perform a pick-and-place exercise after providing informed consent. FMG signals were simultaneously collected using 2 FMG straps worn on their wrist and at the midposition of their forearm, respectively. Raw FMG signals and 3 additional FMG features (ie, root mean square, wavelength, and window symmetry) were extracted and fed into a linear discriminant analysis classifier to predict grasping states. The transition from nongrasping to grasping states was detected during the process of counting the number of grasping actions.ResultsThe median accuracy for detecting grasping events using FMG recorded from the wrist was 95%, and the corresponding interquartile range (IQR) was 5%. For forearm FMG classification, the median accuracy was 92%, and the corresponding IQR was 3%. The difference between the 2 median accuracies was statistically significant (P<.001) when using a paired 2-tailed sign test. The median percentage error for counting grasping events when FMG was recorded from the wrist was 1%, and the corresponding IQR was 2%. The median percentage error for FMG recorded from the forearm was 2%, and the corresponding IQR was also 2%. While the median percentage error for the wrist was lower than that of the forearm, the difference between the 2 was not statistically significant based on a paired 2-tailed sign test (P=.29).ConclusionsThis study reports that grasping can reliably be counted using an unobtrusive and simple FMG strap even in the presence of arm movements. Such a result supports the foundation for future research evaluating the feasibility of monitoring hand grasping during unsupervised ADL, leading to further investigations with individuals with motor function deficits of the arm.

Investigation of Channel Selection for Gesture Classification for Prosthesis Control Using Force Myography: A Case Study

“…It has been demonstrated that unlike EMG, performance of FMG does not rely heavily on feature extraction (Belyea et al, 2018) which has led to the common use of raw FMG signals for gesture recognition (Radmand et al, 2016;Ferigo et al, 2017;Jiang et al, 2017;Xiao and Menon, 2017a;Belyea et al, 2018). Since the focus of this study was channel selection, feature extraction was not considered in this experiment.…”

“…Various classification methods have been assessed for FMG controlled prostheses, some of the commonly used ones of which are linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbors (KNN) (Naik et al, 2015;Ahmadizadeh et al, 2017). Amongst these, LDA is one of the most widely used classifiers due to its capability in separating different classes of gestures and also its computational efficiency (Cho et al, 2016;Radmand et al, 2016;Xiao and Menon, 2017a). A study by Ahmadizadeh et al compared performance of the three aforementioned classifiers for gesture classification using FMG data and determined LDA to be the classifier of choice for their study (Ahmadizadeh et al, 2017).…”

Wrist-worn wearables based on force myography: on the significance of user anthropometry

“…All ML models were trained using data collected while performing static singleton gestures ( Table 2). These models previously demonstrated good accuracy in hand gesture classification based on FMG data [17,47,48]. ML models were then used to identify the singleton gestures performed during compound gestures or dynamic motions.…”

Payload estimation using forcemyography sensors for control of upper-body exoskeleton in load carrying assistance

“…In literature, there are some reported works on using FMG to detect upper body and lower body movements Cho et al (2016); Kadkhodayan et al (2016); Xiao and Menon (2017); Sadarangani and Menon (2017); Islam and Bai (2017); Jiang et al (2016); Xiao et al (2014); Islam et al (2018). However, this approach has not been used yet for payload estimation to control upperbody exoskeleton in load carrying tasks.…”