Grasp Pattern Recognition Using Surface Electromyography Signals and Bayesian-Optimized Support Vector Machines for Low-Cost Hand Prostheses
Source abstract
Abstract
Every year, thousands of people undergo amputations due to trauma or medical conditions. The loss of an upper limb, in particular, has profound physical and psychological consequences for patients. One potential solution is the use of externally powered prostheses equipped with motorized artificial hands. However, these commercially available prosthetic hands are prohibitively expensive for most users. In recent years, advancements in 3D printing and sensor technologies have enabled the design and production of low-cost, externally powered prostheses. This paper presents a pattern-recognition-based human–prosthesis interface that utilizes surface electromyography (sEMG) signals, captured by an affordable device, the Myo armband. A Support Vector Machine (SVM) algorithm, optimized using Bayesian techniques, is trained to classify the user’s intended grasp from among nine common grasping postures essential for daily life activities and functional prosthetic performance. The proposal is viable for real-time implementations on low-cost platforms with 85% accuracy in grasping posture recognition.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Muscle activation and electromyography studies · EEG and Brain-Computer Interfaces · Neuroscience and Neural Engineering
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Luxoft Italy, 10126 Torino, Italy
Departament d’Enginyeria Mecànica i Construcció (EMC), Universitat Jaume I, 12071 Castelló de la Plana, Spain
Instituto de Instrumentación para Imagen Molecular (I3M), Universitat Politècnica de València, 46020 Valencia, Spain
Funding
Spanish Ministry of Economy, Industry and Competitiveness · Universitat Jaume I
References (29)
Show source-provided references
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