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{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T17:50:55Z","timestamp":1732038655930},"reference-count":108,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2019,10,22]],"date-time":"2019-10-22T00:00:00Z","timestamp":1571702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations.<\/jats:p>","DOI":"10.3390\/s19204596","type":"journal-article","created":{"date-parts":[[2019,10,23]],"date-time":"2019-10-23T15:46:59Z","timestamp":1571845619000},"page":"4596","source":"Crossref","is-referenced-by-count":224,"title":["Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation"],"prefix":"10.3390","volume":"19","author":[{"given":"Nawadita","family":"Parajuli","sequence":"first","affiliation":[{"name":"The MARCS Institute, Western Sydney University, Werrington 2747, NSW, Australia"}]},{"given":"Neethu","family":"Sreenivasan","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Mathematics, Western Sydney University, Penrith 2751, NSW, Australia"}]},{"given":"Paolo","family":"Bifulco","sequence":"additional","affiliation":[{"name":"Department of Information Technology and Electrical Engineering, \u201cFederico II\u201d The University of Naples, Via Claudio 10, 80140 Naples, Italy"}]},{"given":"Mario","family":"Cesarelli","sequence":"additional","affiliation":[{"name":"Department of Information Technology and Electrical Engineering, \u201cFederico II\u201d The University of Naples, Via Claudio 10, 80140 Naples, Italy"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1165-4451","authenticated-orcid":false,"given":"Sergio","family":"Savino","sequence":"additional","affiliation":[{"name":"Department of Information Technology and Electrical Engineering, \u201cFederico II\u201d The University of Naples, Via Claudio 10, 80140 Naples, Italy"}]},{"given":"Vincenzo","family":"Niola","sequence":"additional","affiliation":[{"name":"Department of Information Technology and Electrical Engineering, \u201cFederico II\u201d The University of Naples, Via Claudio 10, 80140 Naples, Italy"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0716-8431","authenticated-orcid":false,"given":"Daniele","family":"Esposito","sequence":"additional","affiliation":[{"name":"Department of Information Technology and Electrical Engineering, \u201cFederico II\u201d The University of Naples, Via Claudio 10, 80140 Naples, Italy"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2630-7011","authenticated-orcid":false,"given":"Tara J.","family":"Hamilton","sequence":"additional","affiliation":[{"name":"School of Engineering, Macquarie University, Macquarie Park (NSW), Waterloo road, Sydney 2113, Australia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1790-9838","authenticated-orcid":false,"given":"Ganesh R.","family":"Naik","sequence":"additional","affiliation":[{"name":"The MARCS Institute, Western Sydney University, Werrington 2747, NSW, Australia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0932-5306","authenticated-orcid":false,"given":"Upul","family":"Gunawardana","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Mathematics, Western Sydney University, Penrith 2751, NSW, Australia"}]},{"given":"Gaetano D.","family":"Gargiulo","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Mathematics, Western Sydney University, Penrith 2751, NSW, Australia"},{"name":"Department of Information Technology and Electrical Engineering, \u201cFederico II\u201d The University of Naples, Via Claudio 10, 80140 Naples, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1109\/JBHI.2014.2326660","article-title":"Nonnegative matrix factorization for the identification of EMG finger movements: Evaluation using matrix analysis","volume":"19","author":"Naik","year":"2015","journal-title":"IEEE J. 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