{"id":"W4413112115","doi":"10.1177/00187208251367179","title":"Evaluating the Feasibility of EMG-Based Human–Machine Interfaces for Driving","year":2025,"lang":"en","type":"article","venue":"Human Factors The Journal of the Human Factors and Ergonomics Society","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Science Foundation","keywords":"Human–machine system; Computer science; Human–computer interaction; Human–machine interface; Physical medicine and rehabilitation; 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":[0.002215331,0.000671697,0.0002038584,0.0003141866,0.0001323762,0.0006279357,0.0004674229,0.0006773833,0.003023807],"category_scores_gemma":[0.006399736,0.0001914828,0.0002593976,0.0001162031,0.0002222101,0.0007395293,0.0006496568,0.0001577027,0.0003695069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000184038,"about_ca_system_score_gemma":0.0002172754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003606815,"about_ca_topic_score_gemma":0.0006749047,"domain_scores_codex":[0.9989807,0.0004294094,0.00009177254,0.0001038254,0.0003077581,0.00008648565],"domain_scores_gemma":[0.9974163,0.001392167,0.0002122284,0.00009734546,0.0007437286,0.000138249],"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":[0.007743044,0.003121686,0.06219077,0.002492327,0.0002702261,0.0006157561,0.001631308,0.004883489,0.5548829,0.0004023328,0.00104811,0.360718],"study_design_scores_gemma":[0.001548589,0.1034706,0.5670683,0.0006130405,0.000801745,0.002691834,0.00316343,0.07579456,0.2326592,0.0006766647,0.01129836,0.0002137291],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.983163,0.0004621945,0.01470349,0.00009223681,0.00004132216,0.0003195677,0.00007608082,0.0000880385,0.001054074],"genre_scores_gemma":[0.9754211,0.0003890902,0.02256672,0.00008713151,0.00003800508,0.000347207,0.0001419345,0.00001611903,0.0009926537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003023807,"threshold_uncertainty_score":0.01171589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1193916551269075,"score_gpt":0.4336411568221872,"score_spread":0.3142495016952797,"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."}}