{"id":"W4367849482","doi":"10.32920/22734383.v1","title":"Multidomain Multimodal Fusion For Human Action Recognition Using Inertial Sensors","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pattern recognition (psychology); Computer science; Artificial intelligence; Unavailability; Convolutional neural network; Fusion rules; Fusion; Set (abstract data type); Domain (mathematical analysis); Action recognition; Modality (human–computer interaction); Image fusion; Image (mathematics); Mathematics","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":[0.0004829358,0.0009480127,0.0008061673,0.001466748,0.0001962809,0.000500979,0.0004992789,0.0006124209,0.002206036],"category_scores_gemma":[0.0008888892,0.0002278075,0.0008347573,0.001124831,0.0003433191,0.0008308273,0.0008582809,0.0005417851,0.0008267924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003392824,"about_ca_system_score_gemma":0.0003209341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001976903,"about_ca_topic_score_gemma":0.002488522,"domain_scores_codex":[0.999647,0.00005981712,0.00001764243,0.0001155643,0.0001100805,0.00004978679],"domain_scores_gemma":[0.9997962,0.00004033951,0.00003461904,0.00004261461,0.00006821079,0.00001805919],"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":[0.0004507826,0.0001816232,0.002973804,0.0002211492,0.0001830844,0.0003881901,0.0001189085,0.05407399,0.08318632,0.002723682,0.006117593,0.849381],"study_design_scores_gemma":[0.00001391111,0.0001969684,0.01166579,0.00005190775,0.000105328,0.0004826299,0.000129729,0.9380261,0.03819652,0.006752924,0.004322662,0.00005542762],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0518071,0.00186195,0.9404942,0.0002649166,0.0002030433,0.0000774346,0.0005185136,0.002015696,0.002757065],"genre_scores_gemma":[0.7742903,0.001382231,0.2187907,0.0002769198,0.0002113839,0.0001018298,0.00125627,0.00009894505,0.003591476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002206036,"threshold_uncertainty_score":0.007379889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1486675098306643,"score_gpt":0.3353499357420419,"score_spread":0.1866824259113776,"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."}}