{"id":"W2792397721","doi":"10.1007/s10055-018-0339-2","title":"Virtual grasps recognition using fusion of Leap Motion and force myography","year":2018,"lang":"en","type":"article","venue":"Virtual Reality","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Canada Excellence Research Chairs, Government of Canada","keywords":"Computer science; Artificial intelligence; Computer vision; Linear discriminant analysis; Wearable computer; Virtual reality; Motion capture; Motion (physics); Classifier (UML); Pattern recognition (psychology)","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":[0.0004755324,0.0008623987,0.0009927365,0.001757401,0.0002455637,0.001007244,0.0005353055,0.0009753379,0.002507264],"category_scores_gemma":[0.001238747,0.0003640004,0.0004602736,0.0008425689,0.0003150678,0.001171889,0.001190266,0.0004293173,0.001089912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001912547,"about_ca_system_score_gemma":0.000409522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001507733,"about_ca_topic_score_gemma":0.001895918,"domain_scores_codex":[0.9995448,0.0000613237,0.00003338713,0.0001184045,0.0001767133,0.000065433],"domain_scores_gemma":[0.999605,0.00009038448,0.0000636603,0.00006552056,0.0001297308,0.00004571878],"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":[0.0008841044,0.0001313493,0.005436835,0.0004283292,0.0001671519,0.0005230047,0.0002391269,0.01020047,0.3655578,0.0009948942,0.002604656,0.6128322],"study_design_scores_gemma":[0.0001464568,0.0009413862,0.08196972,0.0002439203,0.0003053598,0.005158733,0.0004579026,0.6661419,0.2284206,0.005053418,0.01087653,0.0002841163],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.137118,0.001065767,0.85438,0.0001746449,0.0001756548,0.0001234368,0.0006006823,0.002619352,0.003742393],"genre_scores_gemma":[0.8524569,0.0006131621,0.1420516,0.0001735716,0.00009078708,0.0001140148,0.0004764512,0.000141656,0.003881762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002507264,"threshold_uncertainty_score":0.008387625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05604011449351804,"score_gpt":0.2896654805709181,"score_spread":0.2336253660774001,"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."}}