{"id":"W4384158823","doi":"10.1109/i2mtc53148.2023.10175973","title":"Machine Learning to Determine Handle Force and Direction Using Strain Gauge Measurements","year":2023,"lang":"en","type":"article","venue":"","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Innovation, Science and Economic Development Canada","funders":"","keywords":"Strain gauge; Computer science; Calibration; Artificial intelligence; Simulation; Control theory (sociology); Engineering; Structural engineering; Physics","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":[0.001449278,0.001072579,0.0007444272,0.001503549,0.0002698756,0.0008021684,0.0008243745,0.0009241112,0.001539525],"category_scores_gemma":[0.006137455,0.0003767557,0.0005014347,0.0009171722,0.0004116691,0.001003451,0.0003977494,0.0007915798,0.001232593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003983602,"about_ca_system_score_gemma":0.0004993189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001925548,"about_ca_topic_score_gemma":0.002287394,"domain_scores_codex":[0.9991794,0.0001608041,0.00006601815,0.0002079111,0.0003227898,0.00006303013],"domain_scores_gemma":[0.9977704,0.000976692,0.0003532898,0.00020444,0.0006442445,0.00005095587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001715709,0.0003726888,0.02787601,0.0001822482,0.0001363671,0.0001041709,0.00009281594,0.185673,0.03055516,0.002365196,0.002506785,0.749964],"study_design_scores_gemma":[0.00001371247,0.0001524402,0.0061132,0.00003817401,0.00001734207,0.00007688795,0.00003998088,0.9777861,0.01230261,0.002394537,0.001035163,0.00002979483],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04349915,0.0002447185,0.9527846,0.0001308569,0.00008397074,0.00008282151,0.0001248436,0.001024597,0.002024419],"genre_scores_gemma":[0.6825474,0.0004057367,0.3133557,0.0001906686,0.00008382802,0.000189044,0.000435215,0.0000865598,0.002705795],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001925548,"threshold_uncertainty_score":0.007664561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0490125447429797,"score_gpt":0.253246099276773,"score_spread":0.2042335545337933,"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."}}