{"id":"W4387961611","doi":"10.1145/3607828.3617791","title":"Memory-Efficient High-Accuracy Food Intake Activity Recognition with 3D mmWave Radars","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Activity recognition; Point cloud; Reduction (mathematics); Artificial intelligence; Radar; Cloud computing; Pattern recognition (psychology); 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":[0.0002032523,0.0006382378,0.0005578112,0.0006330977,0.0001041741,0.0003880495,0.0006476495,0.0004918442,0.001905846],"category_scores_gemma":[0.000753561,0.0003029422,0.0004646199,0.0006110942,0.0001656957,0.0006336436,0.0007294085,0.000501707,0.0021187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001353743,"about_ca_system_score_gemma":0.0002312645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008280011,"about_ca_topic_score_gemma":0.001886788,"domain_scores_codex":[0.9997398,0.00002536755,0.00001142718,0.00006086787,0.0001363809,0.0000261897],"domain_scores_gemma":[0.9997882,0.00004805579,0.00004070158,0.00005416317,0.00005522849,0.00001366029],"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":[0.0003074393,0.0001016169,0.004742043,0.0001984044,0.00008687939,0.0002495115,0.00009397979,0.02841099,0.1694878,0.001003666,0.005078424,0.7902392],"study_design_scores_gemma":[0.00005172021,0.0003685968,0.02065747,0.00006058411,0.00008047045,0.001376286,0.0001219355,0.8149019,0.1452921,0.003556832,0.01346026,0.00007183311],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04491933,0.0005580902,0.9489874,0.0001275072,0.00009347423,0.00005708285,0.000596786,0.002792472,0.001867963],"genre_scores_gemma":[0.5088234,0.0009175968,0.482934,0.0003531091,0.0001085866,0.0001702492,0.002109846,0.0001925344,0.004390663],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001905846,"threshold_uncertainty_score":0.00637573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04278397490467296,"score_gpt":0.2449002386662504,"score_spread":0.2021162637615775,"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."}}