{"id":"W2963993350","doi":"10.1155/2018/7316954","title":"Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors","year":2018,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":346,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Residual; Computer science; Deep learning; Artificial intelligence; Domain (mathematical analysis); Connection (principal bundle); State (computer science); Wearable computer; Pattern recognition (psychology); Long short term memory; Machine learning; Recurrent neural network; Artificial neural network; Algorithm; Engineering; Embedded system; 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":[0.0003336389,0.0007303708,0.0005115453,0.0004143357,0.0001588914,0.0003419433,0.0008598513,0.0005339938,0.002905052],"category_scores_gemma":[0.0008096533,0.0002617822,0.0005520497,0.0007630341,0.0001840276,0.0008026475,0.0006116685,0.0008530465,0.001146257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004116856,"about_ca_system_score_gemma":0.0005704472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005006687,"about_ca_topic_score_gemma":0.01037053,"domain_scores_codex":[0.9997838,0.00004262177,0.00001232325,0.00008363891,0.0000419605,0.00003575379],"domain_scores_gemma":[0.999877,0.00003810488,0.00001599145,0.00002482314,0.00003275449,0.00001135033],"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":[0.0004299316,0.0002884312,0.002514351,0.0003322886,0.0001892841,0.0002231231,0.0001151214,0.08890148,0.0427203,0.003620985,0.0126695,0.8479953],"study_design_scores_gemma":[0.00001179833,0.00009756482,0.002088281,0.0000270566,0.00004256357,0.00008901747,0.00002731516,0.9792345,0.011688,0.003700105,0.002976905,0.00001698771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06778552,0.00342684,0.914501,0.0004616712,0.0004176053,0.0000850107,0.001709014,0.006728387,0.004884888],"genre_scores_gemma":[0.8121578,0.001750045,0.1743577,0.0003651583,0.0001433554,0.0001710459,0.003223604,0.0001411026,0.00769015],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005006687,"threshold_uncertainty_score":0.009955108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06830659696497564,"score_gpt":0.2870241871802647,"score_spread":0.2187175902152891,"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."}}