{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.000666177,0.000183002,0.0001976007,0.0003589539,0.001443404,0.0004106379,0.001943909,0.00005077848,0.0003883964],"category_scores_gemma":[0.00007543426,0.0002281074,0.00007918765,0.0003525505,0.0001110623,0.0008918025,0.001368004,0.0006653193,0.00004644624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003606724,"about_ca_system_score_gemma":0.0001569166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001193779,"about_ca_topic_score_gemma":0.0002688325,"domain_scores_codex":[0.9977687,0.0006299247,0.0003265703,0.0004863101,0.000568114,0.0002203569],"domain_scores_gemma":[0.9976273,0.0003465168,0.000366469,0.001260167,0.0003078924,0.00009170866],"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":[0.00009116971,0.001564337,0.001181204,0.00003935131,0.0003247067,0.00002774138,0.003469269,0.0004830624,0.3800039,0.04909225,0.0009066795,0.5628163],"study_design_scores_gemma":[0.0009981084,0.0002465142,0.001346062,0.0001241331,0.0000289973,0.0002270318,0.00122305,0.9776104,0.004223984,0.008908624,0.004355376,0.0007077105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.883521,0.0001094937,0.07911181,0.007901383,0.001796979,0.0007955527,0.0003363664,0.0004514761,0.02597591],"genre_scores_gemma":[0.9958779,0.00005729781,0.003095735,0.000224325,0.00005805377,0.0001998541,0.00004513282,0.00001755291,0.0004241241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9771274,"threshold_uncertainty_score":0.9998566,"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."}}