{"id":"W3045185643","doi":"","title":"MappEMG: Supporting Musical Expression with Vibrotactile Feedback by Capturing Gestural Features through Electromyography","year":2020,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Music Technology and Sound Studies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Library and Archives Canada; McGill University; Centre for Interdisciplinary Research in Music Media and Technology","funders":"","keywords":"Electromyography; Musical expression; Computer science; Human–computer interaction; Speech recognition; Expression (computer science); Musical; Psychology; Neuroscience","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":[0.0002457113,0.0005540179,0.000454512,0.0002910669,0.0001227731,0.0004739946,0.0006168296,0.0007426795,0.01098843],"category_scores_gemma":[0.0004830136,0.000195193,0.0002120208,0.0001500636,0.0001919747,0.0003853756,0.000974372,0.0002712177,0.002188775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001143479,"about_ca_system_score_gemma":0.0001308071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005427276,"about_ca_topic_score_gemma":0.001622713,"domain_scores_codex":[0.9998617,0.00001958473,0.000004707558,0.00003909137,0.00005923763,0.00001572309],"domain_scores_gemma":[0.9999273,0.00003128234,0.000005087772,0.00001224028,0.000009293252,0.00001475019],"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":[0.001086986,0.0001148099,0.001514561,0.0004408752,0.00008059542,0.0005370899,0.0001928482,0.004242146,0.5080532,0.001492254,0.0193687,0.4628759],"study_design_scores_gemma":[0.0008842147,0.001275887,0.03572777,0.0001628907,0.0001623772,0.003397995,0.0001773289,0.3614136,0.4675277,0.006943207,0.1221323,0.0001947142],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1312219,0.0009422195,0.814051,0.0004027928,0.0004813866,0.0005678757,0.004731593,0.03770598,0.009895256],"genre_scores_gemma":[0.5727186,0.0006283514,0.3906358,0.0006089812,0.0001662078,0.0007291138,0.004038041,0.001835488,0.02863942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01098843,"threshold_uncertainty_score":0.03675991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01045497710052136,"score_gpt":0.2196047531904644,"score_spread":0.209149776089943,"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."}}