{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002525301,0.0005715482,0.0008255115,0.000839894,0.0002313577,0.0003409048,0.0005625348,0.0005591889,0.001433851],"category_scores_gemma":[0.0005894233,0.0001619079,0.0004334428,0.0005142937,0.00009569986,0.0003065285,0.0003215331,0.0002996335,0.0006907998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002160083,"about_ca_system_score_gemma":0.0003269394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003515844,"about_ca_topic_score_gemma":0.003598949,"domain_scores_codex":[0.9997225,0.00002443317,0.00002988292,0.0001044426,0.00008901065,0.00002957096],"domain_scores_gemma":[0.9998574,0.00002217507,0.00001938193,0.00001644216,0.00007152652,0.00001298375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008897396,0.0005429349,0.01893205,0.0001771348,0.0001364986,0.0006078737,0.00008452727,0.02277391,0.0529348,0.0004655767,0.006359207,0.8960958],"study_design_scores_gemma":[0.0001014038,0.0007068193,0.03083254,0.00005674858,0.00009651575,0.000916921,0.00006106481,0.9415284,0.0215908,0.0006653968,0.003406662,0.00003675717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3250484,0.001588825,0.6595505,0.0004029162,0.0003712572,0.0005186773,0.001367413,0.007000179,0.004151855],"genre_scores_gemma":[0.8310682,0.0004999288,0.1631,0.0002002107,0.00009191754,0.000320624,0.001458424,0.00003454661,0.003226143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003515844,"threshold_uncertainty_score":0.006990731,"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."}}