{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004022656,0.0006156182,0.0007005629,0.0005848221,0.0002146758,0.0003552118,0.0008003119,0.0003667967,0.004836249],"category_scores_gemma":[0.0009130873,0.0002425666,0.0003770345,0.0004811025,0.0002110333,0.0006327668,0.0005561236,0.0003052834,0.00223109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001961439,"about_ca_system_score_gemma":0.0002419605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000651033,"about_ca_topic_score_gemma":0.001401516,"domain_scores_codex":[0.9995316,0.00006802594,0.00003869671,0.000167999,0.0001610594,0.0000325198],"domain_scores_gemma":[0.9995907,0.0001109485,0.00006874515,0.0001232891,0.00008452355,0.00002186318],"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":[0.0005501995,0.000195955,0.002596264,0.0002595797,0.00007345917,0.0001597305,0.0001046679,0.01182942,0.1312299,0.001457302,0.003709781,0.8478339],"study_design_scores_gemma":[0.00007196052,0.000735365,0.01023285,0.00006137618,0.00005683411,0.0007392865,0.00008996395,0.8307494,0.141592,0.002495889,0.01309146,0.00008368051],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03819754,0.0003757699,0.9521872,0.00006359856,0.00007265346,0.0001308677,0.0003032949,0.00664323,0.002025878],"genre_scores_gemma":[0.4822057,0.0003257499,0.5095161,0.0001334678,0.00007014985,0.0004212822,0.0008022109,0.0002064185,0.006318974],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004836249,"threshold_uncertainty_score":0.01617891,"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."}}