Abstract
To improve the daily living independence of people with limb impairments, researchers are developing various prosthetic limbs. This study aims to design a dual-modality sensor integrating surface electromyography (sEMG) and force myography (FMG) to measure muscle activities for forearm and hand motion recognition. sEMG records electrical signals from muscle contractions, while FMG measures muscle mechanical deformation during contraction. Combining these two models compensates for individual limitations and increases overall performance. An integrated design of the FMG and sEMG measurement units enables simultaneous measurement while keeping the sensor compact. Using strain gauges to sense FMG instead of traditional force-sensitive resistors can enhance signal stability and sensitivity. The dual-modality sensor combines sEMG and FMG advantages to offer accurate and reliable hand gesture recognition. Experimental results show a 91.8% classification accuracy for recognizing 22 forearm and hand motions using the dual-modal sensor. This technology offers an effective means of controlling prosthetic limbs, improving life quality for individuals with limb impairments, and has potential applications in biomedical engineering, rehabilitation, and robotics.
This research was supported in part by JSPS KAKENHI grant numbers JP23H00166, JP23H03785, JP22K04025, a project commissioned by JSPS and NSFC under the Japan-China Scientific Cooperation Program, and JKA through its promotion funds from KEIRIN RACE.
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Tang, Y. et al. (2023). A Strain Gauge Based FMG Sensor for sEMG-FMG Dual Modal Measurement of Muscle Activity Associated with Hand Gestures. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14268. Springer, Singapore. https://doi.org/10.1007/978-981-99-6486-4_16
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