{"id":"W4308990711","doi":"10.1145/3567710","title":"Leveraging Smartwatch and Earbuds Gesture Capture to Support Wearable Interaction","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Waterloo","funders":"","keywords":"Gesture; Smartwatch; Wearable computer; Computer science; Human–computer interaction; Set (abstract data type); Context (archaeology); Wearable technology; Gesture recognition; Artificial intelligence; Embedded system","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":[0.0009887858,0.0009196438,0.0004272592,0.000815702,0.0003092693,0.001147778,0.0005936768,0.0006601595,0.004142441],"category_scores_gemma":[0.004061017,0.0002930245,0.0004934893,0.0004716763,0.0005040469,0.001428253,0.002170605,0.0003763578,0.0011743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001835944,"about_ca_system_score_gemma":0.0002472906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000590366,"about_ca_topic_score_gemma":0.002335737,"domain_scores_codex":[0.9989285,0.0004212939,0.0000636841,0.0001878798,0.000285619,0.0001129099],"domain_scores_gemma":[0.9979242,0.001303809,0.0001559011,0.0002553959,0.000254761,0.0001058161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001476628,0.0002460163,0.01773143,0.00243624,0.0001607219,0.001746537,0.008367348,0.001721318,0.585389,0.003954938,0.003113775,0.3736561],"study_design_scores_gemma":[0.0004385174,0.006908084,0.2105712,0.002406867,0.0007597533,0.01005112,0.01448891,0.0497881,0.5044723,0.01432485,0.1850441,0.0007462744],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5687827,0.00170698,0.4050151,0.0004057019,0.0001928629,0.0009647129,0.001070828,0.002632315,0.01922874],"genre_scores_gemma":[0.8412871,0.0008001685,0.1489624,0.0003214752,0.00008316617,0.0007949611,0.0005954823,0.0001851929,0.006970082],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004142441,"threshold_uncertainty_score":0.01385784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03947325581111828,"score_gpt":0.2992554450309514,"score_spread":0.2597821892198331,"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."}}