{"id":"W2909467556","doi":"10.1109/ism.2018.000-2","title":"Towards Improved Human Action Recognition Using Convolutional Neural Networks and Multimodal Fusion of Depth and Inertial Sensor Data","year":2018,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Softmax function; Artificial intelligence; Convolutional neural network; Computer science; Pattern recognition (psychology); Classifier (UML); Feature extraction; Action recognition; Support vector machine; Computer vision; Sensor fusion; Activity recognition; Fusion","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":[0.0006861996,0.001376471,0.0008862553,0.0009923012,0.0001824084,0.0005296302,0.0009347385,0.0006276655,0.001852972],"category_scores_gemma":[0.001108251,0.0003499163,0.0007302089,0.000740819,0.0003277168,0.001059778,0.0008921837,0.0007888869,0.0009307376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005032847,"about_ca_system_score_gemma":0.000542947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007452823,"about_ca_topic_score_gemma":0.009768541,"domain_scores_codex":[0.9993986,0.00006789919,0.00002889866,0.0002230345,0.0001778035,0.0001038168],"domain_scores_gemma":[0.9996548,0.00007564803,0.00005694643,0.00006857087,0.0001143849,0.00002959265],"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":[0.0004000872,0.0002732602,0.005213455,0.0001961564,0.0002387415,0.000189814,0.00009245257,0.05580645,0.08277579,0.001616584,0.005302731,0.8478944],"study_design_scores_gemma":[0.00001264646,0.0001502983,0.006975681,0.0000263563,0.00006609596,0.0001717393,0.00004449711,0.9529663,0.03564377,0.001586775,0.002328156,0.00002765957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1064144,0.001834553,0.8803539,0.0002887571,0.0002533344,0.00009144197,0.000623215,0.006381474,0.003758933],"genre_scores_gemma":[0.7590697,0.0007756374,0.2320651,0.0002848542,0.0001308738,0.00007789223,0.00126068,0.0001342058,0.006201067],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007452823,"threshold_uncertainty_score":0.01481885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1067638320971793,"score_gpt":0.328152495443346,"score_spread":0.2213886633461667,"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."}}