{"doi":"10.1109/tmrb.2025.3606364","title":"A Deep Learning Framework With Domain Generalization and Few-Shot Learning for Locomotion Mode Classification Across Users, Sessions, and Prostheses","abstract":"Transfemoral amputees don and doff their prostheses at least daily, making inter-session classification performance important for clinical implementation of locomotion mode classification algorithms. Here, we present a deep-learning framework based on domain-adversarial training and few-shot learning fine-tuning to classify locomotion modes in unseen sessions or subjects' data across different prosthesis models. We validated the approach with a leave-one-session-out analysis repeated five times and made comparisons to a prosthesis-specific classifier. The dataset was created by merging data from two different prosthesis models (Vanderbilt University, VU, Gen 2 and Gen 3 powered knee-ankle prostheses), for a total of 31 sessions acquired across multiple days from 11 subjects. Subjects performed five locomotion tasks: level walking, incline and decline walking, and stair ascent and descent. Since transitions between different locomotion modes happen at different gait events, the analyses have been repeated for both heel-strike (HS) and toe-off (TO) events. At HS events, the proposed approach achieves a median f1-score of 99.12% and 92.41% on VU Gen 2 and Gen 3 prostheses respectively. At TO events, the proposed approach reaches a median f1-score of 96.83% with VU Gen 2 and 94.36% with VU Gen 3. The proposed framework is a promising solution for locomotion classification on data of previously unseen sessions or subjects, allowing classification on multiple prosthesis models.","journal":"IEEE Transactions on Medical Robotics and Bionics","year":2025,"id":574133,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.953,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","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":332335,"name":"Levi J. Hargrove","orcid":"0000-0002-1705-0050","position":2,"is_corresponding":false},{"id":871579,"name":"Eugenio Anselmino","orcid":"0000-0002-6429-3848","position":0,"is_corresponding":true}],"reference_count":25,"raw_metadata":null,"created_at":"2026-07-19T02:57:40.686992Z","pmid":"41924131","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":[]}