{"doi":"10.3389/fbioe.2023.1134135","title":"Autoencoder-based myoelectric controller for prosthetic hands","abstract":"In the past, linear dimensionality-reduction techniques, such as Principal Component Analysis, have been used to simplify the myoelectric control of high-dimensional prosthetic hands. Nonetheless, their nonlinear counterparts, such as Autoencoders, have been shown to be more effective at compressing and reconstructing complex hand kinematics data. As a result, they have a potential of being a more accurate tool for prosthetic hand control. Here, we present a novel Autoencoder-based controller, in which the user is able to control a high-dimensional (17D) virtual hand via a low-dimensional (2D) space. We assess the efficacy of the controller via a validation experiment with four unimpaired participants. All the participants were able to significantly decrease the time it took for them to match a target gesture with a virtual hand to an average of <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"m1\"><mml:mrow><mml:mn>6.9</mml:mn><mml:mi>s</mml:mi></mml:mrow></mml:math> and three out of four participants significantly improved path efficiency. Our results suggest that the Autoencoder-based controller has the potential to be used to manipulate high-dimensional hand systems via a myoelectric interface with a higher accuracy than PCA; however, more exploration needs to be done on the most effective ways of learning such a controller.","journal":"Frontiers in Bioengineering and Biotechnology","year":2023,"id":362405,"datarank":0.5449000285284005,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"self_citation_contribution":0.32958368660043297,"citation_network_contribution":0.21531634192796756,"self_endowment_contribution":0.32958368660043297,"citer_contribution":0.21531634192796756,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":8,"citers_with_citation_signal":6,"citers_with_endowment":6,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9446,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":465775,"name":"Fabio Rizzoglio","orcid":"0000-0002-6744-4605","position":1,"is_corresponding":false},{"id":466692,"name":"Ferdinando A. Mussa-Ivaldi","orcid":null,"position":2,"is_corresponding":false},{"id":302097,"name":"Eric Rombokas","orcid":"0000-0001-8523-1913","position":3,"is_corresponding":false},{"id":465774,"name":"Alexandra A. Portnova-Fahreeva","orcid":"0000-0003-3658-5293","position":0,"is_corresponding":true}],"reference_count":26,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:14:19.266946Z","pmid":"37434753","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}