{"id":"W4386127600","doi":"10.11159/icbb23.111","title":"Fall Detection Algorithm Using a Smart Wearable System for Remote Health Monitoring","year":2023,"lang":"en","type":"article","venue":"Proceedings of the World Congress on New Technologies","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Information Technology Industry Development Agency; Egypt-Japan University of Science and Technology","keywords":"Accelerometer; Wearable computer; Gyroscope; Computer science; Sitting; Artificial intelligence; Stair climbing; Support vector machine; Statistical classification; Real-time computing; Machine learning; Physical medicine and rehabilitation; Simulation; Computer vision; Algorithm; Computer security; Medicine; Embedded system; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072666,0.0002201475,0.0003885365,0.000693878,0.0003742272,0.0002152703,0.001338853,0.0001122544,8.262383e-8],"category_scores_gemma":[0.0002878643,0.0001812373,0.0001472769,0.002133679,0.00006938448,0.0006301084,0.0005973144,0.0002627623,0.00000802234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003435135,"about_ca_system_score_gemma":0.00007986583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003194344,"about_ca_topic_score_gemma":0.00004175205,"domain_scores_codex":[0.9982078,0.00001428526,0.0004099605,0.0004537189,0.0003974752,0.0005167249],"domain_scores_gemma":[0.9985583,0.0001886081,0.0006115136,0.0003672881,0.0002182856,0.00005596166],"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":[0.00001947763,0.00002109176,0.001675645,0.0003575041,0.00005662816,5.210963e-7,0.0001671894,0.00001712393,0.009472082,0.00104223,0.001172615,0.9859979],"study_design_scores_gemma":[0.0008467562,0.0002086094,0.0004865899,0.004419222,0.00002236168,0.00005731103,0.003183694,0.108675,0.8711972,0.003206617,0.007234652,0.0004619258],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6336645,0.002484867,0.2653733,0.02543937,0.02774694,0.009211831,0.00004913899,0.0342949,0.001735052],"genre_scores_gemma":[0.9737655,0.00003768949,0.0245497,0.0000194472,0.0001141745,0.00006585639,1.608569e-7,0.00002848044,0.001419017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.985536,"threshold_uncertainty_score":0.7390643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0518012971219606,"score_gpt":0.295189719000069,"score_spread":0.2433884218781084,"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."}}