{"doi":"10.1109/tbme.2021.3120616","title":"Real-Time Adaptation of an Artificial Neural Network for Transfemoral Amputees Using a Powered Prosthesis","abstract":"OBJECTIVE: We evaluated a two-step method to improve control accuracy for a powered prosthetic leg using machine learning and adaptation, while reducing subject training time. METHODS: First, information from three transfemoral amputees was grouped together, to create a baseline control system that was subsequently tested using data from a fourth subject (user-independent classification). Second, online adaptation was investigated, whereby the fourth subject's data were used to improve the baseline control system in real-time. Results were compared for user-independent classification and for user-dependent classification (data collected from and tested in the same subject), with and without adaptation. RESULTS: The combination of a user-independent classifier with real-time adaptation based on a unique individual's data produced a classification error rate as low as 1.61% [0.15 standard error of the mean (SEM)] without requiring collection of initial training data from that individual. Training/testing using a subject's own data (user-dependent classification), combined with adaptation, resulted in a classification error rate of 0.9% [0.12 SEM], which was not significantly different (P 0.05) but required, on average, an additional 7.52 hours [0.92 SEM] of training sessions. CONCLUSION AND SIGNIFICANCE: We found that the combination of a user-independent dataset with adaptation resulted in error rates that were not significantly different from using a user-dependent dataset. Furthermore, this method eliminated the need for individual training sessions, saving many hours of data collection time.","journal":"IEEE Transactions on Biomedical Engineering","year":2021,"id":171174,"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":27,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9235,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":501128,"name":"Ann M. Simon","orcid":"0000-0002-6431-0329","position":1,"is_corresponding":false},{"id":705742,"name":"Emily A. Seyforth","orcid":null,"position":2,"is_corresponding":false},{"id":332335,"name":"Levi J. Hargrove","orcid":"0000-0002-1705-0050","position":3,"is_corresponding":false},{"id":705087,"name":"Richard B. Woodward","orcid":"0000-0002-3060-5225","position":0,"is_corresponding":true}],"reference_count":34,"raw_metadata":null,"created_at":"2026-07-18T23:46:28.747384Z","pmid":"34652995","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":[]}