{"id":"W3081133800","doi":"10.1109/jiot.2020.3019280","title":"SitR: Sitting Posture Recognition Using RF Signals","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Ergonomics and Musculoskeletal Disorders","field":"Psychology","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Sitting; Wearable computer; Computer science; Artificial intelligence; Computer vision; Biometrics; Embedded system; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003335635,0.0001472579,0.0002458376,0.00009113216,0.00005409847,0.00006927307,0.0002740287,0.0001232299,0.001073997],"category_scores_gemma":[0.00008999596,0.0001405264,0.0002472979,0.00009144126,0.00004300192,0.0002587094,0.00003977485,0.0005604572,0.0001006392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003417639,"about_ca_system_score_gemma":0.00002827642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001650945,"about_ca_topic_score_gemma":0.000001925949,"domain_scores_codex":[0.9988201,0.00008403105,0.0004950016,0.0002025268,0.0001548982,0.0002434647],"domain_scores_gemma":[0.9990751,0.00005866211,0.0004959696,0.00008548333,0.000131782,0.0001529594],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001323389,0.0004146149,0.005789406,0.0002110943,0.001513939,0.000263713,0.07926738,0.0005505286,0.7308037,0.0002984398,0.03279134,0.1467725],"study_design_scores_gemma":[0.04705486,0.02294323,0.06734377,0.01189946,0.004772256,0.0138915,0.1275102,0.1418999,0.4012955,0.07651897,0.07157542,0.01329502],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.984422,0.0003255174,0.006142627,0.0009808737,0.001250609,0.00008338167,0.000005175037,0.00002531385,0.006764453],"genre_scores_gemma":[0.9944974,0.00001866637,0.002215676,0.002657077,0.0004665152,9.417597e-7,0.000003332681,0.00003000053,0.0001103443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3295082,"threshold_uncertainty_score":0.9998391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05538206607993238,"score_gpt":0.3114261153183102,"score_spread":0.2560440492383778,"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."}}