{"id":"W4315786962","doi":"10.3390/s23020849","title":"Novel Deep Learning Network for Gait Recognition Using Multimodal Inertial Sensors","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Artificial intelligence; Gait; Pattern recognition (psychology); Feature (linguistics); Convolutional neural network; Benchmark (surveying); Gyroscope; Inertial measurement unit; Deep learning; Wearable computer; Activity recognition; Computer vision; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001840667,0.0001830743,0.000219123,0.0002028554,0.0001802686,0.0000473582,0.00005395257,0.0001221909,0.00009922253],"category_scores_gemma":[0.0001206311,0.0002025133,0.0001836812,0.0006098818,0.0000216604,0.00007386606,0.00001582852,0.0001943422,0.0002886411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004736695,"about_ca_system_score_gemma":0.000006001138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003804904,"about_ca_topic_score_gemma":0.00004039882,"domain_scores_codex":[0.9989048,0.00003015021,0.0002605289,0.000219193,0.0001375627,0.0004478096],"domain_scores_gemma":[0.9995457,0.0001340712,0.00004558839,0.00009332955,0.00008641976,0.00009491099],"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":[0.00001197438,0.000008401057,0.000184958,0.00003812152,0.00008223729,0.000004829998,0.0001647139,0.9654644,0.01462568,0.000002770893,0.0002123573,0.01919962],"study_design_scores_gemma":[0.000531733,0.00001536147,0.0006853399,0.0000405279,0.00008274143,0.000009452925,0.0003887008,0.9945541,0.001574917,0.00005624101,0.001785516,0.0002753634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9856574,0.00002046254,0.01216831,0.00003472542,0.0004432191,0.0001712531,0.00003024367,0.0009182269,0.0005561435],"genre_scores_gemma":[0.9841523,0.00004838695,0.01409904,0.00004040528,0.0008613678,0.00001987566,0.0003540207,0.00009220043,0.0003324719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02908977,"threshold_uncertainty_score":0.8258255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0328583358889877,"score_gpt":0.2488271803388547,"score_spread":0.215968844449867,"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."}}