{"id":"W4385819714","doi":"10.1109/tim.2023.3304703","title":"Wearable Smart Rings for Multifinger Gesture Recognition Using Supervised Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Concordia University","keywords":"Artificial intelligence; Computer science; Gesture recognition; Support vector machine; Feature selection; Random forest; Pattern recognition (psychology); Gesture; Naive Bayes classifier; Feature extraction; Wearable computer; Accelerometer; Feature vector; k-nearest neighbors algorithm; Normalization (sociology); Feature (linguistics); Gyroscope; Computer vision; Speech recognition; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007507639,0.0001582313,0.0001492027,0.0003020442,0.0005171549,0.0001815954,0.00009722274,0.000080479,0.00001691236],"category_scores_gemma":[0.00001394564,0.0001603722,0.00007806918,0.0004299514,0.00001960745,0.0004345974,0.000001890548,0.0001538708,0.00006871959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001339332,"about_ca_system_score_gemma":0.00005703188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004512719,"about_ca_topic_score_gemma":0.00003713126,"domain_scores_codex":[0.9985232,0.0001067376,0.0002804268,0.0003741216,0.0004630402,0.0002524922],"domain_scores_gemma":[0.999361,0.00006053568,0.000086318,0.0001357178,0.0002521237,0.0001042579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006889929,0.0001391808,0.0001550544,0.0001705948,0.0001167445,0.000002290665,0.003170682,0.00517139,0.07844576,0.00003109545,0.0001301817,0.9123981],"study_design_scores_gemma":[0.009148417,0.0008246616,0.002288077,0.00116327,0.000188946,0.0000697397,0.003447563,0.5713778,0.4000836,0.000657614,0.009516379,0.001233974],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1553489,0.00002031783,0.842131,0.0003993915,0.001021829,0.0006350073,0.000009864896,0.0003104844,0.0001231327],"genre_scores_gemma":[0.9881825,0.00007939042,0.01106132,0.0001809788,0.00004449985,0.0002492038,0.000008737582,0.0000186838,0.0001746983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9111642,"threshold_uncertainty_score":0.6539789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.112728836739005,"score_gpt":0.2825794947282961,"score_spread":0.1698506579892912,"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."}}