{"id":"W4288471702","doi":"10.4017/gt.2022.21.1.590.07","title":"An overview of Markov Chains for monitoring indoor movements and ambient motion detection","year":2022,"lang":"en","type":"article","venue":"Gerontechnology","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain; Hidden Markov model; Computer science; Motion (physics); Motion sensors; Artificial intelligence; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.00036842,0.00009210216,0.0001805048,0.0002531623,0.0001755276,0.00002146629,0.0003524525,0.00006892568,0.000004815113],"category_scores_gemma":[0.00002603095,0.0001083697,0.00003969579,0.000258368,0.00003220482,0.0002726547,0.0003153099,0.0001274422,8.791376e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001211591,"about_ca_system_score_gemma":0.00001565454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008921004,"about_ca_topic_score_gemma":0.00004600972,"domain_scores_codex":[0.9990028,0.00008977719,0.0002240982,0.0003400158,0.000160528,0.0001827378],"domain_scores_gemma":[0.9993046,0.00004250917,0.0001828695,0.0003729402,0.00006666872,0.00003043744],"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":[0.00001835119,0.0001165035,0.003640243,0.00004350812,0.0000229206,0.000001477427,0.0002920123,0.00000812607,0.07926634,0.001303811,0.00000695322,0.9152797],"study_design_scores_gemma":[0.00491289,0.0050478,0.05316595,0.0001208434,0.00004192978,0.0002930498,0.003842026,0.1152944,0.7945716,0.0085565,0.0132049,0.0009481262],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5849379,0.0003205828,0.4135166,0.0001428799,0.0005319941,0.0003750017,0.00001137714,0.0001533668,0.00001037134],"genre_scores_gemma":[0.997719,0.00002506367,0.001758948,0.00003230223,0.00002614688,0.0003931439,0.000002484007,0.000008291943,0.00003464773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9143316,"threshold_uncertainty_score":0.441919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0632937323954968,"score_gpt":0.3088752353800755,"score_spread":0.2455815029845787,"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."}}