{"id":"W4386158794","doi":"10.1109/csci58124.2022.00029","title":"Comparison of Machine Learning Methods for Human Activity Recognition Using Pseudo Free-Living Data","year":2022,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Machine learning; Computer science; Artificial intelligence; Personalization; Task (project management); Activity recognition; Data modeling; Deep learning; Focus (optics); Engineering","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":[0.006142876,0.001542272,0.001227738,0.002376224,0.0004248018,0.001150895,0.001667579,0.001422145,0.001062447],"category_scores_gemma":[0.0133717,0.0002797486,0.001371871,0.001485106,0.0003930175,0.001812639,0.0009780457,0.001366672,0.0007914681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008714707,"about_ca_system_score_gemma":0.0007746436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008895411,"about_ca_topic_score_gemma":0.007983258,"domain_scores_codex":[0.9961622,0.001588274,0.0003672871,0.0008703503,0.0007370805,0.0002747715],"domain_scores_gemma":[0.9918356,0.005579359,0.0002816778,0.0008559677,0.001249835,0.0001976025],"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":[0.001808992,0.001190812,0.03576416,0.0008973182,0.001827182,0.0001972666,0.0002177383,0.2479648,0.003953434,0.002092084,0.009352211,0.694734],"study_design_scores_gemma":[0.00004847201,0.0003680493,0.01732393,0.00009154249,0.00008733613,0.0001134721,0.0001774901,0.9745728,0.002928798,0.002177028,0.0020636,0.00004757975],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6504635,0.01637166,0.3149146,0.001731981,0.00132066,0.0005083459,0.003960892,0.004550926,0.00617751],"genre_scores_gemma":[0.8775148,0.002441593,0.1095355,0.0003635808,0.0001968219,0.0003633631,0.0075992,0.0001935564,0.001791652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008895411,"threshold_uncertainty_score":0.03248698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3306650003447379,"score_gpt":0.465669221059043,"score_spread":0.1350042207143051,"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."}}