Force Myography to Control Robotic Upper Extremity Prostheses: A Feasibility Study
| Autor: | Brittany Pousett, Lukas-Karim Merhi, Richard Chen, Carlo Menon, Zhen Xiao, Erina Cho |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
030506 rehabilitation
medicine.medical_specialty Histology Computer science lcsh:Biotechnology 0206 medical engineering Robotic hand Biomedical Engineering force myography Bioengineering 02 engineering and technology Thumb residual limb 03 medical and health sciences Physical medicine and rehabilitation FMG Force-sensing resistor lcsh:TP248.13-248.65 Assistive technology medicine FSR grip Simulation Original Research Bioengineering and Biotechnology 020601 biomedical engineering body regions medicine.anatomical_structure classification transradial amputee force sensing resistors 0305 other medical science human activities Residual limb Biotechnology |
| Zdroj: | Frontiers in Bioengineering and Biotechnology, Vol 4 (2016) Frontiers in Bioengineering and Biotechnology |
| ISSN: | 2296-4185 |
| DOI: | 10.3389/fbioe.2016.00018 |
| Popis: | Advancement in assistive technology has led to the commercial availability of multi-dexterous robotic prostheses for the upper extremity. The relatively low performance of the currently used techniques to detect the intention of the user to control such advanced robotic prostheses, however, limits their use. This article explores the use of force myography (FMG) as a potential alternative to the well-established surface electro-myography (sEMG). Specifically, the use of FMG to control different grips of a commercially available robotic hand, Bebionic3, are investigated. Four male transradially amputated subjects participated in the study and a protocol was developed to assess the prediction accuracy of eleven grips. Different combinations of grips were examined ranging from six up to eleven grips. The results indicate that it is possible to classify six primary grips important in activities of daily living using FMG with an accuracy of above 70% in the residual limb. Additional strategies to increase classification accuracy, such as using the available modes on the Bebionic3, allowed results to improve up to 88.83% and 89.00% for opposed thumb and non-opposed thumb modes respectively. |
| Databáze: | OpenAIRE |
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