{"id":"W2992942736","doi":"10.3390/s19235325","title":"Lower Body Kinematics Monitoring in Running Using Fabric-Based Wearable Sensors and Deep Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Sagittal plane; Kinematics; Mean squared error; Gait; Gait analysis; Wearable computer; Transverse plane; Computer science; Convolutional neural network; Coronal plane; Simulation; Motion capture; Artificial intelligence; Artificial neural network; Physical medicine and rehabilitation; Mathematics; Engineering; Medicine; Motion (physics); Structural engineering; Statistics; Physics","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":[0.0002349716,0.0004465856,0.0002761453,0.0003044934,0.0001079782,0.000294521,0.000312943,0.0003377412,0.0006458548],"category_scores_gemma":[0.0005059885,0.0002115971,0.0001782512,0.0002768798,0.0001400369,0.0003205008,0.0003000966,0.0001930911,0.0001833033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001707788,"about_ca_system_score_gemma":0.0001720159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001352943,"about_ca_topic_score_gemma":0.005432918,"domain_scores_codex":[0.9998353,0.00002869092,0.00001011852,0.00005829706,0.00005169791,0.00001588762],"domain_scores_gemma":[0.9998456,0.0000385727,0.00004280858,0.00001634889,0.00004643478,0.00001015906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00103758,0.0005046331,0.04556483,0.000582755,0.0002306146,0.0003371804,0.0002477431,0.04223794,0.4779564,0.0005763965,0.001228681,0.4294951],"study_design_scores_gemma":[0.00005802493,0.001458,0.1256042,0.0001272422,0.0002332767,0.0009030692,0.000195222,0.686902,0.1798419,0.001384859,0.003210865,0.00008134027],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6304913,0.0009506439,0.364761,0.0001588987,0.00009213424,0.00006934358,0.0004008,0.0007408209,0.002335088],"genre_scores_gemma":[0.9425773,0.0003999297,0.05542059,0.00007862221,0.00002075791,0.00004853809,0.0001685336,0.00002208424,0.001263627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001352943,"threshold_uncertainty_score":0.002690196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01273843532524079,"score_gpt":0.2266921152982287,"score_spread":0.2139536799729879,"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."}}