{"id":"W3205137388","doi":"10.3390/s21216997","title":"Human Activity Recognition: A Comparative Study to Assess the Contribution Level of Accelerometer, ECG, and PPG Signals","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Activity recognition; Accelerometer; Random forest; Artificial intelligence; Computer science; Photoplethysmogram; Pattern recognition (psychology); Feature selection; Classifier (UML); Curse of dimensionality; Inertial measurement unit; Support vector machine; Speech recognition; Machine learning; Computer vision","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":[0.0007742057,0.0001689603,0.0004223707,0.0001079296,0.0002434644,0.0002202748,0.000293455,0.00004491665,0.00003131952],"category_scores_gemma":[0.0001682925,0.0001412239,0.00007341609,0.0006351887,0.00005892328,0.0003905541,0.0002948611,0.0001718738,0.00003423775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005043637,"about_ca_system_score_gemma":0.00007233576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001856288,"about_ca_topic_score_gemma":0.0007894588,"domain_scores_codex":[0.9975677,0.001052115,0.000309268,0.0004714868,0.0003842693,0.0002152179],"domain_scores_gemma":[0.9977774,0.0006885105,0.0002215198,0.0004686281,0.0007448203,0.00009912188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002772466,0.008119178,0.03353367,0.000165041,0.001996435,0.0003445049,0.06498443,0.0002475242,0.6070598,0.001159593,0.002740897,0.2793716],"study_design_scores_gemma":[0.003342331,0.001420844,0.4006438,0.0002519988,0.0001248046,0.0002085437,0.01232888,0.005184391,0.5733601,0.001428552,0.0007814411,0.0009243297],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9656194,0.00002350568,0.03255219,0.0004635737,0.0001723618,0.0007488059,0.00006057888,0.00005489976,0.0003046787],"genre_scores_gemma":[0.9991909,0.000001245139,0.0004468348,0.0001208465,0.00004472195,0.00004514035,0.000004293965,0.000006580315,0.000139485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3671101,"threshold_uncertainty_score":0.5758945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3954254137585573,"score_gpt":0.3934748632998047,"score_spread":0.001950550458752642,"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."}}