{"id":"W4323318307","doi":"10.3390/bioengineering10030326","title":"Detecting Safety Anomalies in pHRI Activities via Force Myography","year":2023,"lang":"en","type":"article","venue":"Bioengineering","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; New York Institute of Technology","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Artificial intelligence; Wearable computer; Support vector machine; Robot; Human–robot interaction; Simulation; Embedded system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001254176,0.0002007572,0.0001990149,0.0007758665,0.00008219358,0.00003009496,0.0001015636,0.0000641011,0.000009675721],"category_scores_gemma":[0.00001843897,0.0002252817,0.00009062325,0.001563585,0.00002121226,0.0002069244,0.00003232366,0.0001741267,0.000004522769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000531155,"about_ca_system_score_gemma":0.000003593269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001424735,"about_ca_topic_score_gemma":0.00003349167,"domain_scores_codex":[0.9990183,0.000008782831,0.0002177545,0.0001657722,0.0001216216,0.0004677988],"domain_scores_gemma":[0.9996872,0.00009106367,0.00001729096,0.0001483391,0.00001021626,0.00004587205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000037994,0.00003169198,0.02108851,0.0007491952,0.0004243716,0.00003961255,0.003601225,0.3117423,0.3703773,0.00066568,0.0008702994,0.2903718],"study_design_scores_gemma":[0.00128367,0.0001105474,0.4511911,0.0002855691,0.00002851197,0.00001848176,0.002652595,0.4264272,0.1023691,0.0003476575,0.01358512,0.001700456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9726536,0.0006325014,0.0205965,0.00006965736,0.0005221239,0.0001890227,0.000006365324,0.002768926,0.002561274],"genre_scores_gemma":[0.9989896,0.0003317015,0.0004320911,0.00001166272,0.0000830702,0.00005473377,0.000006638049,0.00004881176,0.00004170824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4301026,"threshold_uncertainty_score":0.918672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008471974169748247,"score_gpt":0.1993334700945256,"score_spread":0.1908614959247774,"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."}}