{"id":"W3000247354","doi":"10.1109/tnsre.2020.2966749","title":"Unsupervised Cross-Subject Adaptation for Predicting Human Locomotion Intent","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; Guangdong Province Introduction of Innovative R&D Team; National Natural Science Foundation of China","keywords":"Computer science; Set (abstract data type); Adaptation (eye); Artificial intelligence; Wearable computer; Test set; Machine learning; Independence (probability theory); Robot; Hidden Markov model; Subject (documents); Cross-validation; Pattern recognition (psychology); Psychology; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002613545,0.00103586,0.0009202763,0.0007808213,0.0002880797,0.0004003101,0.0006339611,0.0006774414,0.001094388],"category_scores_gemma":[0.003491611,0.0002979264,0.0008979608,0.0005795841,0.0005092315,0.0005585372,0.000783088,0.0007849198,0.0007092144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002360226,"about_ca_system_score_gemma":0.0005083859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001761125,"about_ca_topic_score_gemma":0.00353465,"domain_scores_codex":[0.9988434,0.0004517308,0.00004879658,0.0004336577,0.0001408939,0.00008147968],"domain_scores_gemma":[0.9983093,0.0007619695,0.00013428,0.0003184514,0.0003892509,0.00008675403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001689462,0.00124461,0.0559887,0.0002548839,0.0006410767,0.0005664867,0.0007790403,0.1385814,0.06809249,0.001412535,0.005229878,0.7255195],"study_design_scores_gemma":[0.00004331286,0.0006963353,0.04640673,0.00003077363,0.0001537898,0.0004125843,0.000181131,0.9301355,0.01670713,0.002484228,0.002688069,0.00006033868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2570797,0.0008872687,0.7376303,0.0001115626,0.0001884645,0.0002156495,0.0003584691,0.001561871,0.001966626],"genre_scores_gemma":[0.8903083,0.0004896844,0.1014277,0.0002248249,0.0001421979,0.0003968133,0.001922241,0.0001906247,0.004897624],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002613545,"threshold_uncertainty_score":0.0138219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01934502510491882,"score_gpt":0.2319770590565551,"score_spread":0.2126320339516363,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}