{"id":"W4376480569","doi":"10.1109/wcnc55385.2023.10119036","title":"ALSensing: Human Activity Recognition using WiFi based on Active Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Simon Fraser University","funders":"HORIZON EUROPE Health","keywords":"Computer science; Baseline (sea); Artificial intelligence; Activity recognition; Deep learning; Machine learning; Training (meteorology); Training set; Active learning (machine learning)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003828065,0.0008518472,0.0007362356,0.0010748,0.0002518292,0.0005357976,0.001156226,0.0005963325,0.00196979],"category_scores_gemma":[0.001101394,0.0002590002,0.0003718969,0.00088152,0.0002855669,0.0009086251,0.001017297,0.0006414509,0.001137131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002385542,"about_ca_system_score_gemma":0.0002851374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002079564,"about_ca_topic_score_gemma":0.003290068,"domain_scores_codex":[0.999543,0.00006751258,0.00002345666,0.0001463569,0.0001583933,0.00006140329],"domain_scores_gemma":[0.9996866,0.0001020165,0.00004810914,0.00005560592,0.00007555246,0.00003207426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005465867,0.0004268342,0.01225227,0.0002446775,0.0001812945,0.0002797171,0.0001405511,0.02211095,0.03108545,0.001360842,0.009616647,0.9217541],"study_design_scores_gemma":[0.00009933629,0.0005347715,0.0142912,0.00004714918,0.00009296101,0.0009884253,0.0001025558,0.9242938,0.04476391,0.004668773,0.01002798,0.00008918122],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07526948,0.001003413,0.8998956,0.0003041748,0.0003290435,0.0002027066,0.001140885,0.0156824,0.006172299],"genre_scores_gemma":[0.8059402,0.0006245624,0.1833324,0.0006311232,0.0002077763,0.0002546644,0.002053371,0.0001725788,0.006783196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002079564,"threshold_uncertainty_score":0.006589592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04967427940833445,"score_gpt":0.269709875892113,"score_spread":0.2200355964837785,"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."}}