{"id":"W3116667534","doi":"10.2196/21926","title":"Deep Learning–Based Multimodal Data Fusion: Case Study in Food Intake Episodes Detection Using Wearable Sensors","year":2020,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Orionin Tutkimussäätiö; Academy of Finland","keywords":"Computer science; Activity recognition; Sensor fusion; Artificial intelligence; Discriminative model; Wearable computer; Machine learning; Generalizability theory; Dimensionality reduction; Classifier (UML); Deep learning; Pattern recognition (psychology); Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00112444,0.0002272524,0.0004170465,0.0002237055,0.0005208215,0.0001572695,0.0003793924,0.0001066032,0.000007592138],"category_scores_gemma":[0.0001517546,0.0002344792,0.00002998624,0.0008199836,0.00003361439,0.0007364426,0.0003395381,0.0006079291,0.0000128777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001525883,"about_ca_system_score_gemma":0.0003479591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005264298,"about_ca_topic_score_gemma":0.008148445,"domain_scores_codex":[0.9966733,0.0009287174,0.0005856538,0.0009508722,0.0003333436,0.0005281507],"domain_scores_gemma":[0.9981812,0.0002896866,0.0003040244,0.0005882636,0.00008789908,0.0005489718],"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":[0.000428849,0.001363213,0.1102966,0.001982524,0.0000429795,0.001863264,0.03277195,0.003658335,0.0002165736,0.00001718393,0.00003712677,0.8473214],"study_design_scores_gemma":[0.001941899,0.001301195,0.01112006,0.00005561709,0.00001137339,0.0005491581,0.004556153,0.9799374,0.00002967003,0.00001047998,0.000253684,0.0002333529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9121185,0.0002565359,0.08511794,0.0009616892,0.0002045436,0.001100019,0.000009768767,0.0002187957,0.00001224512],"genre_scores_gemma":[0.9970695,0.00002347467,0.002079592,0.0006106994,0.0001480447,0.00003837017,0.000006744623,0.00002021573,0.000003302678],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.976279,"threshold_uncertainty_score":0.9561785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.172212174706064,"score_gpt":0.3786604843711602,"score_spread":0.2064483096650962,"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."}}