{"id":"W2019151722","doi":"10.4018/ijrat.2013010103","title":"Ambient Activity Recognition in Smart Environments for Cognitive Assistance","year":2013,"lang":"en","type":"article","venue":"International Journal of Robotics Applications and Technologies","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Université du Québec à Chicoutimi","funders":"","keywords":"Ambient intelligence; Activity recognition; Context (archaeology); Computer science; Field (mathematics); Cognition; Data science; Key (lock); Human–computer interaction; Artificial intelligence; Psychology; Computer security","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":[0.000614303,0.0004468986,0.0005990452,0.0006012766,0.0002939412,0.001509689,0.0007042848,0.0006653382,0.0019129],"category_scores_gemma":[0.002783013,0.0001872076,0.0003437592,0.0005230333,0.0006217586,0.00186848,0.001289356,0.0005067103,0.001004275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002620246,"about_ca_system_score_gemma":0.0003632445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009262612,"about_ca_topic_score_gemma":0.001177564,"domain_scores_codex":[0.9994635,0.0001943552,0.00003393178,0.0001270093,0.0001402394,0.00004090367],"domain_scores_gemma":[0.9989938,0.0005891402,0.00009713227,0.0001171284,0.0001416736,0.00006100916],"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":[0.0004270332,0.0003342374,0.004143823,0.0006976675,0.0000783426,0.0006115568,0.002168856,0.05716443,0.0460263,0.05051353,0.004913969,0.8329203],"study_design_scores_gemma":[0.00006199394,0.0004566462,0.01112767,0.0003257743,0.0001234344,0.001324753,0.002257385,0.775686,0.04233524,0.1077236,0.0584121,0.0001654651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04157481,0.001642501,0.9477046,0.0003744254,0.0001239153,0.00009034749,0.0000617043,0.001229941,0.00719789],"genre_scores_gemma":[0.6973758,0.001676509,0.2957785,0.000201015,0.0001326553,0.0001239758,0.0001794533,0.00007039191,0.004461676],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0019129,"threshold_uncertainty_score":0.006399274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02617318281638532,"score_gpt":0.2696279258238843,"score_spread":0.243454743007499,"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."}}