{"id":"W3203444931","doi":"10.3390/electronics10202558","title":"Automated Workers’ Ergonomic Risk Assessment in Manual Material Handling Using sEMG Wearable Sensors and Machine Learning","year":2021,"lang":"en","type":"preprint","venue":"Electronics","topic":"Ergonomics and Musculoskeletal Disorders","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Lift (data mining); Decision tree; Wearable computer; Manual handling; Random forest; Computer science; Material handling; Machine learning; Back injury; Support vector machine; Artificial intelligence; Simulation; Engineering; Industrial engineering; Operations management; Physical therapy; Medicine","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":[0.0005846117,0.0006030457,0.0004137115,0.0009558813,0.0001811025,0.000489372,0.0003366516,0.0006111259,0.000743145],"category_scores_gemma":[0.001888856,0.0001714056,0.0003227998,0.0005702241,0.0001848387,0.0004550332,0.0003568112,0.0002384746,0.0003050341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001781529,"about_ca_system_score_gemma":0.0002237984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00166946,"about_ca_topic_score_gemma":0.003028288,"domain_scores_codex":[0.9995566,0.000117487,0.00002213939,0.00008184231,0.0001915494,0.00003031289],"domain_scores_gemma":[0.9992592,0.0003492341,0.0001838955,0.00004819371,0.0001342907,0.00002522309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008470735,0.0008637014,0.1302457,0.0004778452,0.0001855358,0.000354398,0.0005553599,0.09733403,0.066028,0.000580565,0.001415618,0.7011122],"study_design_scores_gemma":[0.00003732204,0.001111133,0.2508861,0.000104349,0.00007060097,0.0004150617,0.000470742,0.7176998,0.02610506,0.002163417,0.0008730382,0.00006341583],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7977647,0.0004712356,0.1982806,0.0001581304,0.00004993797,0.0001168562,0.0002317032,0.0005378118,0.002388896],"genre_scores_gemma":[0.9641466,0.0002032534,0.03467588,0.00003719448,0.00001755014,0.00004300541,0.0001193093,0.00001223134,0.0007450278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00166946,"threshold_uncertainty_score":0.003319502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0143227905732527,"score_gpt":0.3074585566590392,"score_spread":0.2931357660857865,"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."}}