{"id":"W4221105034","doi":"10.3390/s22072631","title":"KinectGaitNet: Kinect-Based Gait Recognition Using Deep Convolutional Neural Network","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gait; Convolutional neural network; Artificial intelligence; Computer science; Deep learning; Inference; Representation (politics); Machine learning; Pattern recognition (psychology); Computer vision; Physical medicine and rehabilitation","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":[0.0003145018,0.001301485,0.0006846717,0.001046475,0.0002134233,0.0005001893,0.001254129,0.000647897,0.00648406],"category_scores_gemma":[0.0009718589,0.0005388499,0.0006014041,0.0007467328,0.0002289492,0.0007503426,0.000918527,0.0007387471,0.002431083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005388996,"about_ca_system_score_gemma":0.0007501799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009094297,"about_ca_topic_score_gemma":0.01643902,"domain_scores_codex":[0.999697,0.00002317741,0.00002254591,0.00009248391,0.0001312516,0.00003366017],"domain_scores_gemma":[0.9998494,0.00002633404,0.00002548367,0.00002235834,0.00005700779,0.00001937553],"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":[0.001259898,0.0004068153,0.008363336,0.001013082,0.0004216793,0.0005268328,0.0001133501,0.06166435,0.0558963,0.004042472,0.07195741,0.7943345],"study_design_scores_gemma":[0.0001253935,0.0002639357,0.01270392,0.0001724425,0.00009198036,0.0006756258,0.00004237006,0.9200354,0.03967942,0.003966054,0.02212525,0.0001182406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05093025,0.002813287,0.8608422,0.0004022853,0.0004994785,0.000585735,0.02569676,0.04955116,0.008678974],"genre_scores_gemma":[0.4404432,0.001949816,0.4870158,0.0007610114,0.00007919729,0.001222994,0.04911669,0.001343785,0.01806754],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009094297,"threshold_uncertainty_score":0.02169132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02219075222480557,"score_gpt":0.2114265206684704,"score_spread":0.1892357684436648,"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."}}