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. 2023 Mar 1;23(5):2716.
doi: 10.3390/s23052716.

Assessment of Low-Density Force Myography Armband for Classification of Upper Limb Gestures

Mustafa Ur Rehman  1 , Kamran Shah  1   2 , Izhar Ul Haq  1 , Sajid Iqbal  3 , Mohamed A Ismail  2 , Fatih Selimefendigil  2
Affiliations

Assessment of Low-Density Force Myography Armband for Classification of Upper Limb Gestures

Mustafa Ur Rehman et al. Sensors (Basel). .

Abstract

Using force myography (FMG) to monitor volumetric changes in limb muscles is a promising and effective alternative for controlling bio-robotic prosthetic devices. In recent years, there has been a focus on developing new methods to improve the performance of FMG technology in the control of bio-robotic devices. This study aimed to design and evaluate a novel low-density FMG (LD-FMG) armband for controlling upper limb prostheses. The study investigated the number of sensors and sampling rate for the newly developed LD-FMG band. The performance of the band was evaluated by detecting nine gestures of the hand, wrist, and forearm at varying elbow and shoulder positions. Six subjects, including both fit and amputated individuals, participated in this study and completed two experimental protocols: static and dynamic. The static protocol measured volumetric changes in forearm muscles at the fixed elbow and shoulder positions. In contrast, the dynamic protocol included continuous motion of the elbow and shoulder joints. The results showed that the number of sensors significantly impacts gesture prediction accuracy, with the best accuracy achieved on the 7-sensor FMG band arrangement. Compared to the number of sensors, the sampling rate had a lower influence on prediction accuracy. Additionally, variations in limb position greatly affect the classification accuracy of gestures. The static protocol shows an accuracy above 90% when considering nine gestures. Among dynamic results, shoulder movement shows the least classification error compared to elbow and elbow-shoulder (ES) movements.

Keywords: accuracy; dynamic protocol; force myography (FMG); gestures; static protocol.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Exploded view of FSR sensor with housing.
Figure 2
Figure 2
(a) FMG armband with 7S sensors, (b) 3D printed sensor, (c) Data acquisition hardware, and (d) LD-FMG band fastened on individual’s forearm.
Figure 3
Figure 3
Flow chart of the experimental setup.
Figure 4
Figure 4
Upper limb gestures: (a) Relax, (b) fingers extended, (c) power, (d) tripod, (e) finger point, (f) wrist flexion, (g) wrist extension, (h) forearm supination, and (i) forearm Pronation.
Figure 4
Figure 4
Upper limb gestures: (a) Relax, (b) fingers extended, (c) power, (d) tripod, (e) finger point, (f) wrist flexion, (g) wrist extension, (h) forearm supination, and (i) forearm Pronation.
Figure 5
Figure 5
Dynamic motions of upper limb (a) elbow, (b) shoulder, and (c) ES movement.
Figure 6
Figure 6
Effect of sensor’s quantity on individual performance.
Figure 7
Figure 7
Effect of sampling rates on individual performance.
Figure 8
Figure 8
Confusion matrices of (a) elbow, (b) shoulder, and (c) ES movements; F: finger point, FE: finger extension, FP: forearm pronation, FS: forearm supination, P: power gesture, R: relax, TP: tripod, WE: wrist extension, and WF: wrist flexion.
Figure 8
Figure 8
Confusion matrices of (a) elbow, (b) shoulder, and (c) ES movements; F: finger point, FE: finger extension, FP: forearm pronation, FS: forearm supination, P: power gesture, R: relax, TP: tripod, WE: wrist extension, and WF: wrist flexion.
Figure 9
Figure 9
Comparison of static and dynamic protocol on hand gestures classification performance.
Figure 10
Figure 10
Comparison of static and dynamic protocol on wrist and forearm gestures classification performance.
See this image and copyright information in PMC

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