{"id":"W2112759888","doi":"10.1109/ainaw.2007.209","title":"Integration of Smart Home Technologies in a Health Monitoring System for the Elderly","year":2007,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":166,"is_retracted":false,"has_abstract":true,"ca_institutions":"Élisabeth Bruyère Hospital; Carleton University","funders":"","keywords":"Residence; Perspective (graphical); Assisted living; Function (biology); Home automation; Computer science; Cognition; Elderly people; Health care; Independent living; Human–computer interaction; Risk analysis (engineering); Gerontology; Business; Medicine; Telecommunications","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":[0.0003154189,0.000266209,0.0002573784,0.0003328923,0.0002579705,0.0005204428,0.0002941711,0.0005974902,0.0008276957],"category_scores_gemma":[0.0008400537,0.0001501001,0.0002200604,0.0001928817,0.0001540533,0.0007199853,0.0003763942,0.000250057,0.0002999399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001575525,"about_ca_system_score_gemma":0.0002822603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008835508,"about_ca_topic_score_gemma":0.001364825,"domain_scores_codex":[0.9997912,0.00005396112,0.00001926402,0.0000457324,0.00006970645,0.00002003378],"domain_scores_gemma":[0.999762,0.00008501991,0.00002472735,0.0000286793,0.00007631966,0.00002337666],"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":[0.0008171641,0.0006654505,0.02008974,0.0003532254,0.0001573848,0.001788077,0.001895638,0.008183253,0.2199609,0.01049931,0.007744631,0.7278452],"study_design_scores_gemma":[0.0002977004,0.00563588,0.07516932,0.0003944453,0.0008381443,0.009547413,0.00127545,0.3937897,0.3337722,0.013032,0.1659193,0.0003284024],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2848606,0.00132995,0.6953004,0.0006798579,0.0001948648,0.0004005346,0.0002669719,0.004607701,0.01235916],"genre_scores_gemma":[0.7479533,0.000632763,0.2460751,0.0003328811,0.00008574859,0.0001636655,0.000190648,0.00003833302,0.004527476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008835508,"threshold_uncertainty_score":0.002768934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04993743631752613,"score_gpt":0.3043630397847111,"score_spread":0.254425603467185,"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."}}