A Wearable Force Myography-Based Armband for Recognition of Upper Limb Gestures

Force myography (FMG) represents a promising alternative to surface electromyography (EMG) in the context of controlling bio-robotic hands. In this study, we built upon our prior research by introducing a novel wearable armband based on FMG technology, which integrates force-sensitive resistor (FSR)...

Full description

Saved in:
Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 23; no. 23; p. 9357
Main Authors Rehman, Mustafa Ur, Shah, Kamran, Haq, Izhar Ul, Iqbal, Sajid, Ismail, Mohamed A
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 23.11.2023
Subjects
Amputation
Data entry
Design
Electrodes
Electromyography
Force
force myography
force-sensitive resistor
gesture recognition
Muscle contraction
Muscle function
Prostheses
Sensors
United Kingdom
force-sensitive resistor
force myography
gesture recognition
Online AccessGet full text

Cover

More Information
Summary:Force myography (FMG) represents a promising alternative to surface electromyography (EMG) in the context of controlling bio-robotic hands. In this study, we built upon our prior research by introducing a novel wearable armband based on FMG technology, which integrates force-sensitive resistor (FSR) sensors housed in newly designed casings. We evaluated the sensors' characteristics, including their load-voltage relationship and signal stability during the execution of gestures over time. Two sensor arrangements were evaluated: arrangement A, featuring sensors spaced at 4.5 cm intervals, and arrangement B, with sensors distributed evenly along the forearm. The data collection involved six participants, including three individuals with trans-radial amputations, who performed nine upper limb gestures. The prediction performance was assessed using support vector machines (SVMs) and k-nearest neighbor (KNN) algorithms for both sensor arrangments. The results revealed that the developed sensor exhibited non-linear behavior, and its sensitivity varied with the applied force. Notably, arrangement B outperformed arrangement A in classifying the nine gestures, with an average accuracy of 95.4 ± 2.1% compared to arrangement A's 91.3 ± 2.3%. The utilization of the arrangement B armband led to a substantial increase in the average prediction accuracy, demonstrating an improvement of up to 4.5%.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s23239357