{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003065776,0.001233295,0.0007089779,0.0007113883,0.0002131618,0.0003224709,0.0009257072,0.0006678724,0.002346397],"category_scores_gemma":[0.00062629,0.0003482463,0.0005809679,0.000704165,0.0001652346,0.0006996582,0.0006268892,0.0006810938,0.0008607145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005916965,"about_ca_system_score_gemma":0.0005380341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009591751,"about_ca_topic_score_gemma":0.01444194,"domain_scores_codex":[0.9997982,0.00001833369,0.00001463987,0.00007879992,0.00004766002,0.00004231046],"domain_scores_gemma":[0.9998827,0.00001949194,0.00001731218,0.00001524658,0.00005341038,0.0000118225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002933749,0.0003286619,0.003922873,0.0001689549,0.0001600746,0.0002203183,0.00003501512,0.104917,0.02282078,0.001348615,0.01368285,0.8521015],"study_design_scores_gemma":[0.00001350275,0.00007937849,0.001455127,0.00001595395,0.00002312651,0.00005548248,0.000007873909,0.9917687,0.004176726,0.0007794995,0.001613947,0.00001069354],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1366221,0.004151278,0.8366506,0.0005888126,0.0006995808,0.0001945827,0.002919483,0.01163805,0.006535671],"genre_scores_gemma":[0.7447367,0.001310034,0.2286582,0.000690956,0.0001478261,0.0002825359,0.008547723,0.0001710608,0.0154549],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009591751,"threshold_uncertainty_score":0.01907188,"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."}}