{"id":"W2895083861","doi":"10.3390/s18103329","title":"Gait Type Analysis Using Dynamic Bayesian Networks","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Gait analysis; Gait; Bayesian probability; Dynamic Bayesian network; Computer science; Type (biology); Bayesian network; Physical medicine and rehabilitation; Artificial intelligence; Medicine; Biology","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":[0.001274684,0.0008108543,0.0008011058,0.003038537,0.0003758687,0.0009454452,0.0009032441,0.0006998589,0.001678719],"category_scores_gemma":[0.004808842,0.0004531583,0.0008961068,0.001434574,0.0003514884,0.001047463,0.0004540122,0.0006249395,0.0006091729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008650104,"about_ca_system_score_gemma":0.0005381977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009850623,"about_ca_topic_score_gemma":0.008855734,"domain_scores_codex":[0.999292,0.0002294518,0.00004935558,0.0001801171,0.0001890813,0.00006004434],"domain_scores_gemma":[0.9989336,0.0005682166,0.0001809984,0.00005873647,0.000217934,0.00004053139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002484752,0.0001633652,0.01374492,0.0001047649,0.0002249295,0.0001673941,0.00007017038,0.6705009,0.003840329,0.0082237,0.001775285,0.3009358],"study_design_scores_gemma":[0.000004141204,0.00001617398,0.001406011,0.00001123337,0.00001622928,0.00004247364,0.00001068604,0.9933548,0.0003466041,0.004445858,0.0003362461,0.000009571194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02457904,0.0003078186,0.9723046,0.000147702,0.00003952358,0.00008153627,0.000298584,0.0004018718,0.001839291],"genre_scores_gemma":[0.7690408,0.0007525757,0.2248532,0.0001422752,0.0001146758,0.0002477423,0.00123213,0.00009035194,0.003526271],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009850623,"threshold_uncertainty_score":0.01958656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009373543899570104,"score_gpt":0.2354328714805125,"score_spread":0.2260593275809424,"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."}}