{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003608261,0.0004739009,0.0002774524,0.0006697617,0.0001061408,0.0003661524,0.0002337566,0.000511178,0.0003768369],"category_scores_gemma":[0.001726434,0.000133579,0.000206608,0.0003281691,0.0002372133,0.0003717581,0.0003521132,0.0002767897,0.0002087579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001228409,"about_ca_system_score_gemma":0.0001591406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009780513,"about_ca_topic_score_gemma":0.001556285,"domain_scores_codex":[0.9997571,0.00004873578,0.00001252719,0.0000863018,0.00006465302,0.0000307976],"domain_scores_gemma":[0.999512,0.0002236859,0.00009404967,0.00004551359,0.00009189467,0.00003297996],"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":[0.0009230748,0.0002908544,0.1153129,0.0004932681,0.0001298316,0.0009222178,0.0008270883,0.01808145,0.5125574,0.0005788328,0.0007997408,0.3490835],"study_design_scores_gemma":[0.00004382273,0.001987296,0.4578397,0.000115622,0.0001312484,0.00280957,0.000671658,0.4140898,0.1179054,0.001849998,0.002475683,0.00008014167],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.829615,0.0005059535,0.167457,0.0001228445,0.00003019799,0.0001102425,0.0003260808,0.0004608921,0.001371793],"genre_scores_gemma":[0.9783661,0.0001908764,0.02085914,0.00002985162,0.00001593244,0.00002432374,0.0001226247,0.00001324016,0.0003777596],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009780513,"threshold_uncertainty_score":0.001944721,"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."}}