{"id":"W4381745408","doi":"10.1109/radarconf2351548.2023.10149586","title":"Light-Weight Learning Model with Patch Embeddings for Radar-based Fall Event Classification: A Multi-domain Decision Fusion Approach","year":2023,"lang":"en","type":"article","venue":"","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Radar; Machine learning; Deep learning; Event (particle physics); Multilayer perceptron; Transfer of learning; Pattern recognition (psychology); Data mining; Artificial neural network; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002630456,0.0001956183,0.0002155254,0.0002594725,0.0001711085,0.0000591788,0.0001098036,0.000100796,0.00004461397],"category_scores_gemma":[0.00002324238,0.0001517913,0.0001455934,0.0005838869,0.000009499831,0.00009831256,0.00001853837,0.0001506723,0.00007898458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006532535,"about_ca_system_score_gemma":0.00002275925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007694925,"about_ca_topic_score_gemma":0.00003652657,"domain_scores_codex":[0.998852,0.00002022248,0.000272771,0.0003139868,0.0002647949,0.0002762704],"domain_scores_gemma":[0.9995026,0.0000820815,0.00004555527,0.000159993,0.00009978956,0.0001099678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008469958,0.000161324,0.000977813,0.0001577511,0.00009730949,0.000003378385,0.0004721142,0.9463947,0.01939617,0.000292515,0.004048238,0.02791399],"study_design_scores_gemma":[0.001267246,0.00003240897,0.0002991456,0.00004787697,0.00003826861,0.000001175652,0.0005361291,0.9930468,0.001314147,0.0001444731,0.003033302,0.0002390177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.154574,0.00001527494,0.8426828,0.0001789136,0.00003243578,0.0002592335,0.000004999494,0.0007004665,0.001551905],"genre_scores_gemma":[0.7969668,0.00002492194,0.201008,0.00004521749,0.00003456004,0.0001722148,0.0002047982,0.00005408891,0.001489455],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6423928,"threshold_uncertainty_score":0.6189868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02694722555436738,"score_gpt":0.2506733122632925,"score_spread":0.2237260867089251,"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."}}