{"doi":"10.1109/tbme.2025.3608674","title":"Contrastive Learning Model for Wearable-Based Ataxia Assessment","abstract":"OBJECTIVE: Frequent and objective assessment of ataxia severity is essential for tracking disease progression and evaluating the effectiveness of potential treatments. Wearable-based assessments have emerged as a promising solution. However, existing methods rely on inertial data features directly correlated with subjective and coarse clinician-evaluated rating scales, which serve as imperfect gold standards. This approach may introduce biases and restrict flexibility in feature design. To address these limitations, this study introduces a novel contrastive learning-based model that leverages motor severity differences in wearable inertial data to learn relevant features. METHODS: The model was trained on inertial data collected from 87 individuals with diagnostically heterogeneous ataxias and 44 healthy participants performing the finger-to-nose task. A pairwise contrastive loss function was proposed to learn representations capturing relative differences in ataxia severity, which were evaluated through downstream regression and classification tasks. RESULTS: The learned features demonstrated strong cross-sectional (r = 0.84) and longitudinal (r = 0.68) associations with clinical scores and robust measurement reliability (intraclass correlation coefficient = 0.96). Additionally, the model exhibited strong known-group validity, distinguishing between ataxia and healthy phenotypes with an area under the receiver operating characteristic curve of 0.95. CONCLUSION: The proposed contrastive model captures robust representations of disease severity with reduced reliance on clinical scales, outperforming state-of-the-art methods that derive features directly from clinical scores. SIGNIFICANCE: Combining wearable sensors with contrastive learning enables a more objective, scalable, and frequent method for assessing ataxia severity, with the potential to enhance patient monitoring and improve clinical trial efficiency.","journal":"IEEE Transactions on Biomedical Engineering","year":2025,"id":574578,"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":0.9514,"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":655145,"name":"Brandon Oubre","orcid":"0000-0002-1807-4775","position":1,"is_corresponding":false},{"id":559138,"name":"Jean‐François Daneault","orcid":"0000-0002-8530-1827","position":2,"is_corresponding":false},{"id":270763,"name":"Christopher D. Stephen","orcid":"0000-0002-4727-192X","position":3,"is_corresponding":false},{"id":228120,"name":"Jeremy D. Schmahmann","orcid":"0000-0003-0706-5125","position":4,"is_corresponding":false},{"id":457083,"name":"Anoopum S. Gupta","orcid":"0000-0002-8741-0621","position":5,"is_corresponding":false},{"id":272441,"name":"Sunghoon Ivan Lee","orcid":"0000-0001-5935-125X","position":6,"is_corresponding":false},{"id":918289,"name":"Juhyeon Lee","orcid":"0000-0002-6877-6329","position":0,"is_corresponding":true}],"reference_count":34,"raw_metadata":null,"created_at":"2026-07-19T02:57:44.572630Z","pmid":"40928918","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":[]}