{"id":"W3037699712","doi":"10.22215/etd/2020-14078","title":"Exploiting Wi-Fi Channel State Information for Artificial Intelligence-Based Human Activity Recognition of Similar Dynamic Motions","year":2020,"lang":"en","type":"dissertation","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Activity recognition; Activities of daily living; Computer science; Sitting; Artificial intelligence; Support vector machine; Motion (physics); Random forest; Assisted living; Ambient intelligence; Measure (data warehouse); Machine learning; Pattern recognition (psychology); Data mining","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":[0.000184593,0.0003327962,0.000209528,0.0006085357,0.0001355985,0.0004586903,0.0001812506,0.0002687369,0.001427729],"category_scores_gemma":[0.0007403349,0.00009399752,0.0002810744,0.0006478927,0.0001727474,0.0003733277,0.0001800809,0.0002238838,0.0006188263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001536983,"about_ca_system_score_gemma":0.0001971764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001598135,"about_ca_topic_score_gemma":0.003302923,"domain_scores_codex":[0.9998726,0.0000217549,0.0000085504,0.00003461198,0.00004467256,0.00001775436],"domain_scores_gemma":[0.9998348,0.00007707137,0.00001920726,0.0000233612,0.00003798239,0.000007593927],"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":[0.0003520528,0.0003613328,0.01202412,0.0001642996,0.000106351,0.0001582137,0.0001446262,0.01819682,0.192369,0.001080753,0.001502856,0.7735397],"study_design_scores_gemma":[0.00004070679,0.0008843392,0.1209147,0.00008304144,0.0002216325,0.000648951,0.0001738847,0.6764344,0.1877226,0.00350587,0.0092847,0.00008504914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4751457,0.0009596006,0.511463,0.0002684658,0.0001477343,0.0001510968,0.0006929521,0.001335901,0.009835476],"genre_scores_gemma":[0.9049982,0.0006906126,0.08902612,0.00008315895,0.00005624268,0.00007576238,0.0008751384,0.00003362865,0.004161171],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001598135,"threshold_uncertainty_score":0.004776239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09051213046755253,"score_gpt":0.3136247882720762,"score_spread":0.2231126578045237,"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."}}