{"id":"W3199713003","doi":"10.1109/thms.2021.3107256","title":"Phase Variable Based Recognition of Human Locomotor Activities Across Diverse Gait Patterns","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Gait; Adaptability; Computer science; STRIDE; Mathematics; Machine learning; Physical medicine and rehabilitation; Medicine; Biology","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":[0.000201824,0.0002929489,0.0003635074,0.0007839716,0.0001108622,0.000332571,0.0002302785,0.0003150696,0.001135122],"category_scores_gemma":[0.0007001571,0.0001003862,0.0002272885,0.0006031006,0.0001397001,0.0003022628,0.0002185175,0.0001933588,0.0004548868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009025112,"about_ca_system_score_gemma":0.0001474724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000651002,"about_ca_topic_score_gemma":0.0012399,"domain_scores_codex":[0.9998382,0.00002475571,0.00001132387,0.00005915162,0.00005145586,0.00001497895],"domain_scores_gemma":[0.9997751,0.00007235058,0.00003834881,0.00002467648,0.0000746192,0.00001482719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003572445,0.0001037725,0.0122365,0.0001778336,0.0000679458,0.0001306853,0.0001440817,0.01064684,0.1239849,0.0007957336,0.001538348,0.8498161],"study_design_scores_gemma":[0.00006943117,0.0006762217,0.1425167,0.00007235276,0.0001411299,0.002105433,0.0002929617,0.7506657,0.09380982,0.002773511,0.006789088,0.00008768004],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.236671,0.0005145387,0.7578598,0.00007762522,0.00007308185,0.0001085689,0.000450235,0.001155159,0.003089824],"genre_scores_gemma":[0.8422443,0.0004091055,0.1544572,0.00005318151,0.00005307015,0.00009742588,0.0006343851,0.00007745373,0.001973835],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001135122,"threshold_uncertainty_score":0.003797352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03814033113644814,"score_gpt":0.2931912207926373,"score_spread":0.2550508896561892,"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."}}