{"id":"W4292230836","doi":"10.1109/icc45855.2022.9839267","title":"Improving Human Activity Recognition using ML and Wearable Sensors","year":2022,"lang":"en","type":"article","venue":"ICC 2022 - IEEE International Conference on Communications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Accelerometer; Linear discriminant analysis; Decision tree; Context (archaeology); Wearable computer; Artificial intelligence; Activity recognition; Machine learning; Wearable technology; Random forest; Gyroscope; Field (mathematics); Identification (biology); Discriminant; Ranging; Statistical classification; Pattern recognition (psychology); Embedded system; Engineering; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000940101,0.001382712,0.001364628,0.002200108,0.000233421,0.0009368459,0.0009905966,0.0007759935,0.001616764],"category_scores_gemma":[0.003144161,0.000237631,0.0008872143,0.001510869,0.0001867777,0.001237975,0.0009461332,0.0007615493,0.002091575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003169964,"about_ca_system_score_gemma":0.0004125277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004400596,"about_ca_topic_score_gemma":0.005010301,"domain_scores_codex":[0.998693,0.0002759079,0.0001019977,0.000414875,0.0003738493,0.0001403181],"domain_scores_gemma":[0.9988766,0.0004326866,0.0001263896,0.0001340694,0.0003593557,0.00007085412],"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":[0.0005268348,0.001143872,0.01851928,0.0004727797,0.0002958213,0.000192627,0.00007197919,0.03818359,0.01845867,0.0003910945,0.01174742,0.9099961],"study_design_scores_gemma":[0.0001005251,0.000572637,0.04070091,0.0001003648,0.0001394106,0.0003708614,0.00015714,0.9220484,0.02532736,0.001324922,0.009094633,0.00006283015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3695233,0.01105654,0.5790632,0.001155595,0.001219282,0.0003714453,0.006804398,0.02139636,0.009409881],"genre_scores_gemma":[0.7997491,0.002387009,0.181492,0.0004912762,0.0003738527,0.0002705086,0.0102617,0.0001506794,0.004823982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004400596,"threshold_uncertainty_score":0.008749962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2347226026849331,"score_gpt":0.368779414392379,"score_spread":0.134056811707446,"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."}}