{"id":"W4394564303","doi":"10.1109/access.2024.3386351","title":"Adaptive Hierarchical Classification for Human Activity Recognition Using Inertial Measurement Unit (IMU) Time-Series Data","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Bhabha Atomic Research Centre; Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Interpretability; Computer science; Inertial measurement unit; Artificial intelligence; Activity recognition; AdaBoost; Decision tree; Random forest; Machine learning; Data mining; Classifier (UML); Boosting (machine learning); Units of measurement; Pattern recognition (psychology)","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001166673,0.0002192785,0.0002559091,0.0002612572,0.0003206808,0.001217153,0.001484469,0.0001206389,0.00002523507],"category_scores_gemma":[0.0001324862,0.0002201272,0.00008190833,0.0005600748,0.00007697919,0.005825702,0.0004236579,0.0002517438,0.0000814185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000223347,"about_ca_system_score_gemma":0.0003076481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002569238,"about_ca_topic_score_gemma":0.0003151453,"domain_scores_codex":[0.9975744,0.0002912686,0.000340327,0.0008921068,0.0006011097,0.0003007329],"domain_scores_gemma":[0.9981285,0.0002647711,0.0001554824,0.0009114537,0.0004365543,0.0001032308],"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":[0.0001558295,0.0002468429,0.00008185273,0.0001987664,0.0002091268,0.00001492032,0.0003838124,0.00003003218,0.2235725,0.000662023,0.002643989,0.7718003],"study_design_scores_gemma":[0.0007975798,0.0002679282,0.001915396,0.0006161217,0.0001269115,0.00006204265,0.00004346472,0.9137997,0.06575246,0.008549292,0.007251479,0.0008176141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08352672,0.0000537311,0.9127093,0.000678989,0.00123115,0.0007873811,0.0001972606,0.0004210505,0.0003944154],"genre_scores_gemma":[0.9958816,0.000003494979,0.003114635,0.00005982111,0.0006220173,0.0001300749,0.00009521353,0.00002850285,0.00006464314],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9137697,"threshold_uncertainty_score":0.9998197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5144295705122292,"score_gpt":0.4129896695537845,"score_spread":0.1014399009584447,"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."}}