{"id":"W3137926756","doi":"10.1007/s11036-021-01741-5","title":"Alternative Deep Learning Architectures for Feature-Level Fusion in Human Activity Recognition","year":2021,"lang":"en","type":"article","venue":"Mobile Networks and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Concatenation (mathematics); Fuse (electrical); Deep learning; Artificial intelligence; Convolutional neural network; Domain (mathematical analysis); Convolution (computer science); Feature (linguistics); Pattern recognition (psychology); Sensor fusion; Activity recognition; Machine learning; Raw data; Feature learning; Artificial neural network","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":[0.0008227178,0.0007593029,0.0007165516,0.0005075741,0.0002648361,0.0008722221,0.001291126,0.001081697,0.002370332],"category_scores_gemma":[0.00172389,0.0003171418,0.0006611166,0.000942572,0.0003565708,0.001400033,0.001263304,0.001357525,0.0008385849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005780518,"about_ca_system_score_gemma":0.0007560758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004963159,"about_ca_topic_score_gemma":0.006847603,"domain_scores_codex":[0.9995949,0.00008114674,0.00002591539,0.0001247014,0.00008184811,0.00009163074],"domain_scores_gemma":[0.9995841,0.0001204603,0.00003409506,0.00007582708,0.0001535893,0.00003184678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005172985,0.0003603231,0.002640957,0.0001618367,0.0002394396,0.00008261658,0.000114269,0.2019985,0.02404713,0.0124195,0.003582534,0.7538356],"study_design_scores_gemma":[0.000007912912,0.00006545416,0.001010785,0.00001578706,0.00002891162,0.00003251108,0.00001577832,0.9869307,0.005239747,0.005831755,0.0008092247,0.00001137055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03783586,0.001013554,0.9577278,0.000395519,0.00009641279,0.0000344492,0.0002221436,0.0007542723,0.001919943],"genre_scores_gemma":[0.815416,0.0007781954,0.1761987,0.0003060522,0.00008938296,0.00009344368,0.000669171,0.00007019315,0.006378811],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004963159,"threshold_uncertainty_score":0.009868562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03160857824323783,"score_gpt":0.2845803037102191,"score_spread":0.2529717254669813,"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."}}