{"id":"W4381415876","doi":"10.1109/tim.2023.3271722","title":"Low-Latency Gesture Recognition From Spatial Filtering of Single-Element Ultrasound Signals","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"West China Hospital, Sichuan University","keywords":"Gesture; Computer science; Gesture recognition; Latency (audio); Artificial intelligence; Usability; Computer vision; Frame rate; Low latency (capital markets); Frame (networking); Speech recognition; Pattern recognition (psychology); Human–computer interaction; Telecommunications","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":[0.0002582385,0.0005401123,0.0003826416,0.0005485012,0.0001205656,0.0004353195,0.0002552375,0.0004499395,0.001335396],"category_scores_gemma":[0.001169743,0.000181479,0.0002561446,0.0004727149,0.0002227642,0.0005611029,0.0003344172,0.0002707764,0.0005874648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001295976,"about_ca_system_score_gemma":0.0002371627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006847607,"about_ca_topic_score_gemma":0.001713698,"domain_scores_codex":[0.9997688,0.00002683465,0.00001536081,0.0000430848,0.0001206252,0.00002530621],"domain_scores_gemma":[0.9996137,0.0002003354,0.00005097207,0.00004252982,0.00007436776,0.00001809924],"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":[0.0003054273,0.00004308812,0.001304237,0.0002406698,0.00002504157,0.0001314096,0.00008036828,0.00339519,0.7041519,0.0004641126,0.0003248326,0.2895336],"study_design_scores_gemma":[0.00002838269,0.0007187426,0.03137571,0.00005625794,0.00008377983,0.001435592,0.0001260892,0.2747622,0.6855044,0.001381789,0.004457368,0.00006987586],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3145477,0.0007312275,0.680424,0.00008856138,0.0001135572,0.00009362621,0.0002044329,0.0009664278,0.002830495],"genre_scores_gemma":[0.7485285,0.0007754667,0.2468462,0.00009284437,0.00006365376,0.00007645922,0.0002223341,0.00008319214,0.003311469],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001335396,"threshold_uncertainty_score":0.004467309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04256171623441891,"score_gpt":0.2239995119834991,"score_spread":0.1814377957490802,"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."}}