{"id":"W2581129691","doi":"10.1109/tnsre.2017.2659730","title":"Rhythmic Extended Kalman Filter for Gait Rehabilitation Motion Estimation and Segmentation","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Extended Kalman filter; Kalman filter; Computer science; Artificial intelligence; Gait; Motion capture; Acceleration; Computer vision; Segmentation; Accelerometer; Motion (physics); Control theory (sociology); Physical medicine and rehabilitation; Medicine","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":[0.0006643267,0.0006338729,0.0007875132,0.0006247495,0.0002432853,0.0004013088,0.0005801857,0.0005976747,0.001790241],"category_scores_gemma":[0.002103733,0.0003534256,0.0005484277,0.0006935988,0.0002223273,0.0006144383,0.0004150785,0.0005675539,0.000733504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000405732,"about_ca_system_score_gemma":0.0008696901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01002747,"about_ca_topic_score_gemma":0.008774325,"domain_scores_codex":[0.9996542,0.00006954248,0.00003045384,0.0001208461,0.00009179518,0.00003315495],"domain_scores_gemma":[0.9997181,0.0001017178,0.00004387834,0.00003659169,0.00009060513,0.0000091526],"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":[0.0002126172,0.00005639228,0.002162993,0.0001718186,0.0001039294,0.00008910722,0.00008597763,0.3409745,0.01739777,0.006498913,0.003516326,0.6287296],"study_design_scores_gemma":[0.000007524099,0.00003282921,0.0009504978,0.00001360063,0.00001123479,0.00003248051,0.000008737386,0.9934565,0.002138281,0.001316236,0.002021682,0.00001036821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002913135,0.0002512606,0.9958363,0.00003197154,0.00003100121,0.0000128175,0.00005602182,0.0004492227,0.0004182636],"genre_scores_gemma":[0.4029672,0.001248567,0.5879851,0.0001429208,0.0001605507,0.0002275937,0.0008894643,0.0001743377,0.006204211],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01002747,"threshold_uncertainty_score":0.01993823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01769125022682044,"score_gpt":0.3157094590321859,"score_spread":0.2980182088053655,"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."}}