{"id":"W2152975774","doi":"10.1109/mis.2015.18","title":"Exploiting Passive RFID Technology for Activity Recognition in Smart Homes","year":2015,"lang":"en","type":"article","venue":"IEEE Intelligent Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec","keywords":"Trilateration; Computer science; Activity recognition; Set (abstract data type); Home automation; Human–computer interaction; Cognition; Smart environment; Representation (politics); Computer security; Artificial intelligence; Internet of Things; Telecommunications; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001173764,0.0003010688,0.0005778863,0.0008462465,0.00009890495,0.0002708635,0.0007431378,0.0002777722,0.000003249892],"category_scores_gemma":[0.0004862997,0.0003098956,0.0001331277,0.0009469335,0.00006021549,0.001030486,0.0001280698,0.0002794337,0.0003496225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005167215,"about_ca_system_score_gemma":0.0001985202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005762035,"about_ca_topic_score_gemma":0.0002697526,"domain_scores_codex":[0.9972512,0.0002948949,0.0006910572,0.000764076,0.0004284009,0.0005703541],"domain_scores_gemma":[0.9974958,0.000555786,0.000459375,0.0006378732,0.0006631212,0.0001880037],"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":[0.000152016,0.0006343899,0.008668421,0.0004665359,0.0001858581,0.0001103032,0.004133307,0.0003756492,0.01026926,0.002182115,0.004535358,0.9682868],"study_design_scores_gemma":[0.00650655,0.002309235,0.0008368932,0.004476646,0.00008658913,0.001451495,0.02074716,0.2200994,0.6334565,0.02457952,0.08114766,0.004302324],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2951017,0.0002433816,0.6970158,0.0005807889,0.004536022,0.001489025,0.00002639848,0.0004615985,0.0005452772],"genre_scores_gemma":[0.9970839,0.00001537305,0.00100368,0.00004759042,0.0003031003,0.001255998,0.000008646439,0.0000315603,0.0002501296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9639844,"threshold_uncertainty_score":0.9999353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1193569879046984,"score_gpt":0.3058545690809873,"score_spread":0.1864975811762889,"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."}}