{"id":"W3000902087","doi":"10.1109/tnsre.2020.2968912","title":"An Effective and Efficient Method for Detecting Hands in Egocentric Videos for Rehabilitation Applications","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Spinal Cord Injury Research","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Rick Hansen Institute","keywords":"Computer science; Wearable computer; Reset (finance); Computer vision; Artificial intelligence; Detector; Process (computing); Embedded system","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":[0.0008895374,0.001155864,0.0009912862,0.003147023,0.0003805393,0.0008579612,0.000995431,0.0008381599,0.002530598],"category_scores_gemma":[0.002882092,0.0003798876,0.0007679614,0.001445067,0.0002526293,0.0009499997,0.0009049109,0.0006320562,0.00191311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005156273,"about_ca_system_score_gemma":0.0008892802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00485822,"about_ca_topic_score_gemma":0.01030636,"domain_scores_codex":[0.9988691,0.000135288,0.0000670054,0.0004129865,0.0004171527,0.00009835949],"domain_scores_gemma":[0.9989991,0.000236716,0.0001433409,0.0001116715,0.000459161,0.00004999014],"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":[0.000201919,0.0001737558,0.005196961,0.0001718108,0.0001292363,0.0001196767,0.00005227379,0.005906981,0.05555835,0.0004264147,0.008983968,0.9230786],"study_design_scores_gemma":[0.0001233424,0.0006529014,0.06161537,0.0001716,0.0003046849,0.002028407,0.0001838094,0.7954507,0.1103208,0.002486236,0.02651634,0.0001457785],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07500221,0.002100839,0.9089693,0.0002665837,0.0004044946,0.000426701,0.001522275,0.007311118,0.003996443],"genre_scores_gemma":[0.3366677,0.001607733,0.6497089,0.0002962326,0.0003162141,0.0004430804,0.002577372,0.0003260577,0.008056773],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00485822,"threshold_uncertainty_score":0.009659886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04086755255755051,"score_gpt":0.2988021272136632,"score_spread":0.2579345746561127,"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."}}