{"id":"W2582827436","doi":"10.1016/j.asoc.2017.01.034","title":"Detecting falls with X-Factor Hidden Markov Models","year":2017,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hidden Markov model; Computer science; Outlier; Identification (biology); Machine learning; Artificial intelligence; Activity recognition; Wearable computer; Training (meteorology); Activities of daily living; Pattern recognition (psychology); Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005009827,0.0002993512,0.0003906732,0.000111878,0.001307319,0.001334253,0.001836841,0.0001069212,0.00000659955],"category_scores_gemma":[0.00006715275,0.0002809777,0.00008048897,0.0001468001,0.00007988809,0.0009357324,0.0009611602,0.0003420188,0.00009747756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008382932,"about_ca_system_score_gemma":0.00009741759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001234379,"about_ca_topic_score_gemma":0.00005520209,"domain_scores_codex":[0.9977513,0.00005662529,0.0003572759,0.0007905858,0.0004776444,0.0005665547],"domain_scores_gemma":[0.997166,0.0004827713,0.0005945654,0.001447789,0.0001458785,0.0001629503],"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":[0.000014532,0.00003610959,0.001477113,0.00003561058,0.00006287779,0.00002339724,0.001966847,0.0002255434,0.001821148,0.002474171,0.0000368956,0.9918258],"study_design_scores_gemma":[0.002943286,0.0001526424,0.01266637,0.0005001524,0.00003447946,0.0002434478,0.0006354547,0.9650847,0.008618626,0.006447163,0.000741079,0.001932579],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2271441,0.00001837593,0.7575912,0.0001237251,0.0002732858,0.00033956,0.000001808166,0.000507562,0.01400046],"genre_scores_gemma":[0.9325293,6.559926e-7,0.06697099,0.000172591,0.0001993986,0.00001774692,0.000001050316,0.00003379563,0.0000745001],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9898932,"threshold_uncertainty_score":0.9999928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03580944863826014,"score_gpt":0.2518386743013393,"score_spread":0.2160292256630791,"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."}}