{"id":"W3095649671","doi":"10.1016/j.gaitpost.2020.10.026","title":"Lower body kinematics estimation from wearable sensors for walking and running: A deep learning approach","year":2020,"lang":"en","type":"article","venue":"Gait & Posture","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":79,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bioinformatics Solutions (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kinematics; Inertial measurement unit; Motion capture; Correlation coefficient; Computer science; Ground reaction force; Gait; Artificial intelligence; Mean squared error; Mathematics; Geodesy; Simulation; Geology; Physical medicine and rehabilitation; Motion (physics); Statistics; Physics; Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002708595,0.0007365788,0.0007544456,0.0005023346,0.0001832479,0.0004558901,0.0005425708,0.0006931393,0.001137791],"category_scores_gemma":[0.0007722302,0.0003502918,0.0006568393,0.0006541681,0.0001450537,0.0004076584,0.0004995046,0.0008873914,0.0005966005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001910917,"about_ca_system_score_gemma":0.0004950206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00768508,"about_ca_topic_score_gemma":0.01241314,"domain_scores_codex":[0.9998595,0.00001571307,0.00000961001,0.00005035497,0.00002843176,0.00003637525],"domain_scores_gemma":[0.9998442,0.0000483209,0.00002336412,0.00001743593,0.00005326311,0.00001333035],"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":[0.0002960477,0.0006781156,0.01457293,0.0001561226,0.0002508346,0.0001716493,0.00006197458,0.1695868,0.02163306,0.0008885153,0.003541345,0.7881625],"study_design_scores_gemma":[0.000009345844,0.00009245586,0.00978833,0.00002866045,0.00004002448,0.00006396393,0.00002043646,0.9860535,0.002493039,0.0008869409,0.0005123721,0.00001089861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.162855,0.001759966,0.8305898,0.000334041,0.0001890266,0.00006142785,0.0007281259,0.00120433,0.002278283],"genre_scores_gemma":[0.9337791,0.0008305882,0.0591197,0.0001652591,0.00007915361,0.00006848561,0.0009732408,0.00003577989,0.004948762],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00768508,"threshold_uncertainty_score":0.01528066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02382599711684696,"score_gpt":0.3157504270403183,"score_spread":0.2919244299234713,"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."}}