{"id":"W2921850771","doi":"10.5430/air.v8n1p25","title":"Body sensor networks for monitoring performances in sports: A brief overview and some new thoughts","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Wearable computer; Computer science; Focus (optics); Wireless sensor network; Point (geometry); Human–computer interaction; Multimedia; Embedded system; Computer network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0008589444,0.0008905665,0.0006470652,0.001576888,0.0003356325,0.001368358,0.0009074282,0.0014258,0.002519242],"category_scores_gemma":[0.001036405,0.0004869495,0.000599493,0.001833303,0.0006233485,0.003090497,0.0006864704,0.002268725,0.001399077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005685973,"about_ca_system_score_gemma":0.0006004503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009694258,"about_ca_topic_score_gemma":0.0009350712,"domain_scores_codex":[0.9995259,0.0001060426,0.00006775769,0.0001202974,0.0001464176,0.00003355228],"domain_scores_gemma":[0.9991962,0.0004548942,0.0000533329,0.00002674001,0.0002307349,0.00003808381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001694257,0.0001715,0.001665589,0.01745118,0.0001701899,0.0006572856,0.0004558924,0.004575422,0.006442146,0.0584755,0.05370172,0.8560641],"study_design_scores_gemma":[0.00001245386,0.0003248774,0.001860243,0.003242922,0.0001117469,0.001558395,0.0002815906,0.003405596,0.001391558,0.01594784,0.9717711,0.00009169718],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0009781616,0.9749231,0.01156595,0.002484877,0.002275764,0.00004534708,0.00006928803,0.00005578397,0.007601681],"genre_scores_gemma":[0.007776719,0.9729081,0.008150359,0.001468247,0.005259288,0.00007187428,0.0001392587,0.00002019844,0.004205961],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002519242,"threshold_uncertainty_score":0.008427739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2029045541022969,"score_gpt":0.4145204979329958,"score_spread":0.2116159438306989,"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."}}