{"id":"W4283752138","doi":"10.1109/i2mtc48687.2022.9806694","title":"InARMS: Individual Activity Recognition of Multiple Subjects with FMCW radar","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Radar; Doppler radar; Artificial intelligence; Activity recognition; Support vector machine; Real-time computing; Machine learning; Radar engineering details; Radar imaging; Telecommunications","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":[0.0006330422,0.000733554,0.0006186008,0.0006589741,0.000114055,0.0003750362,0.0004879578,0.0005555067,0.001463058],"category_scores_gemma":[0.0007457116,0.0001521385,0.0003002191,0.0004347859,0.0001992308,0.0004800226,0.0005931547,0.0003748799,0.001203307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009370113,"about_ca_system_score_gemma":0.0001629076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002562228,"about_ca_topic_score_gemma":0.0005951273,"domain_scores_codex":[0.9995661,0.0001059025,0.00002220824,0.0001197256,0.0001462541,0.00003986371],"domain_scores_gemma":[0.9997401,0.00008496796,0.00004179644,0.00004492338,0.00006343579,0.00002475542],"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":[0.0006867208,0.0003189472,0.008762715,0.0003534888,0.0001480156,0.0002634518,0.000193477,0.003893101,0.2149567,0.001181846,0.004758604,0.7644829],"study_design_scores_gemma":[0.000247893,0.002877474,0.1019866,0.0001413095,0.0004716452,0.006939498,0.000411368,0.5291703,0.3130788,0.004168787,0.04033242,0.0001738181],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1070815,0.001244136,0.880237,0.0001778344,0.000291697,0.0001562205,0.0004799584,0.004803002,0.005528636],"genre_scores_gemma":[0.5275977,0.001002189,0.4599342,0.0004981941,0.0002917971,0.0002316268,0.001024939,0.0001461335,0.009273218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001463058,"threshold_uncertainty_score":0.004894435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06226488939048446,"score_gpt":0.2484723671684397,"score_spread":0.1862074777779553,"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."}}