dc.contributor.author | Maskeliūnas, Rytis | |
dc.contributor.author | Damaševičius, Robertas | |
dc.contributor.author | Raudonis, Vidas | |
dc.contributor.author | Adomavičienė, Aušra | |
dc.contributor.author | Raistenskis, Juozas | |
dc.contributor.author | Griškevičius, Julius | |
dc.date.accessioned | 2023-09-18T16:39:59Z | |
dc.date.available | 2023-09-18T16:39:59Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 2076-3417 | |
dc.identifier.other | (crossref_id)147630002 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/115697 | |
dc.description.abstract | One of the most difficult components of stroke therapy is regaining hand mobility. This research describes a preliminary approach to robot-assisted hand motion therapy. Our objectives were twofold: First, we used machine learning approaches to determine and describe hand motion patterns in healthy people. Surface electrodes were used to collect electromyographic (EMG) data from the forearm’s flexion and extension muscles. The time and frequency characteristics were used as parameters in machine learning algorithms to recognize seven hand gestures and track rehabilitation progress. Eight EMG sensors were used to capture each contraction of the arm muscles during one of the seven actions. Feature selection was performed using the Pareto front. Our system was able to reconstruct the kinematics of hand/finger movement and simulate the behaviour of every motion pattern. Analysis has revealed that gesture categories substantially overlap in the feature space. The correlation of the computed joint trajectories based on EMG and the monitored hand movement was 0.96 on average. Moreover, statistical research conducted on various machine learning setups revealed a 92% accuracy in measuring the precision of finger motion patterns. | eng |
dc.format | PDF | |
dc.format.extent | p. 1-17 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | Science Citation Index Expanded (Web of Science) | |
dc.relation.isreferencedby | Scopus | |
dc.relation.isreferencedby | INSPEC | |
dc.rights | Laisvai prieinamas internete | |
dc.source.uri | https://talpykla.elaba.lt/elaba-fedora/objects/elaba:164986149/datastreams/MAIN/content | |
dc.title | BiomacEMG: a Pareto-optimized system for assessing and recognizing hand movement to track rehabilitation progress | |
dc.type | Straipsnis Web of Science DB / Article in Web of Science DB | |
dcterms.license | Creative Commons – Attribution – 4.0 International | |
dcterms.references | 66 | |
dc.type.pubtype | S1 - Straipsnis Web of Science DB / Web of Science DB article | |
dc.contributor.institution | Kauno technologijos universitetas | |
dc.contributor.institution | Vilniaus universitetas | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.faculty | Mechanikos fakultetas / Faculty of Mechanics | |
dc.subject.researchfield | T 009 - Mechanikos inžinerija / Mechanical enginering | |
dc.subject.researchfield | T 010 - Matavimų inžinerija / Measurement engineering | |
dc.subject.researchfield | T 007 - Informatikos inžinerija / Informatics engineering | |
dc.subject.researchfield | M 001 - Medicina / Medicine | |
dc.subject.studydirection | E02 - Bioinžinerija / Bioengineering | |
dc.subject.vgtuprioritizedfields | MC0404 - Bionika ir biomedicinos inžinerinės sistemos / Bionics and Biomedical Engineering Systems | |
dc.subject.ltspecializations | L105 - Sveikatos technologijos ir biotechnologijos / Health technologies and biotechnologies | |
dc.subject.en | hand motion recognition | |
dc.subject.en | electromyography | |
dc.subject.en | Pareto optimization | |
dc.subject.en | assisted living | |
dcterms.sourcetitle | Applied sciences | |
dc.description.issue | iss. 9 | |
dc.description.volume | vol. 13 | |
dc.publisher.name | MDPI | |
dc.publisher.city | Basel | |
dc.identifier.doi | 147630002 | |
dc.identifier.doi | 2-s2.0-85159278314 | |
dc.identifier.doi | 1 | |
dc.identifier.doi | 000987232600001 | |
dc.identifier.doi | 10.3390/app13095744 | |
dc.identifier.elaba | 164986149 | |