{"doi":"10.1109/tbme.2023.3274053","title":"Functionally Adaptive Myosite Selection Using High-Density sEMG for Upper Limb Myoelectric Prostheses","abstract":"Objective: Our study defines a novel electrode placement method called Functionally Adaptive Myosite Selection (FAMS), as a tool for rapid and effective electrode placement during prosthesis fitting. We demonstrate a method for determining electrode placement that is adaptable towards individual patient anatomy and desired functional outcomes, agnostic to the type of classification model used, and provides insight into expected classifier performance without training multiple models. Methods: FAMS relies on a separability metric to rapidly predict classifier performance during prosthesis fitting. Results: The results show a predictable relationship between the FAMS metric and classifier accuracy (3.45%SE), allowing estimation of control performance with any given set of electrodes. Electrode configurations selected using the FAMS metric show improved control performance ( $p< .05$ ) for target electrode counts compared to established methods when using an ANN classifier, and equivalent performance ( $R^{2}\\geq.96$ ) to previous top-performing methods on an LDA classifier, with faster convergence ( $p< .001$ ). We used the FAMS method to determine electrode placement for two amputee subjects by using the heuristic to search through possible sets, and checking for saturation in performance vs electrode count. The resulting configurations that averaged 95.8% of the highest possible classification performance using a mean 25 number of electrodes (19.5% of the available sites). Significance: FAMS can be used to rapidly approximate the tradeoffs between increased electrode count and classifier performance, a useful tool during prosthesis fitting.","journal":"IEEE Transactions on Biomedical Engineering","year":2023,"id":374761,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9581,"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":495287,"name":"Christopher L. Hunt","orcid":"0000-0001-7647-1886","position":1,"is_corresponding":false},{"id":1136714,"name":"Sapna Kumar","orcid":"0000-0003-1907-2971","position":2,"is_corresponding":false},{"id":1136715,"name":"Joseph Betthauser","orcid":"0000-0002-7173-1703","position":3,"is_corresponding":false},{"id":463958,"name":"Denis Routkevitch","orcid":"0000-0002-7144-6347","position":4,"is_corresponding":false},{"id":1067783,"name":"Rahul R. Kaliki","orcid":"0000-0003-3200-5546","position":5,"is_corresponding":false},{"id":495292,"name":"Nitish V. Thakor","orcid":"0000-0002-9981-9395","position":6,"is_corresponding":false},{"id":1136713,"name":"Rebecca J. Greene","orcid":"0000-0002-1060-4743","position":0,"is_corresponding":true}],"reference_count":46,"raw_metadata":null,"created_at":"2026-07-19T01:16:15.380731Z","pmid":"37192038","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":[]}