{"id":"W4385187201","doi":"10.1109/iwcmc58020.2023.10182728","title":"Efficient Fall Detection using Bidirectional Long Short-Term Memory","year":2023,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Context (archaeology); Term (time); Machine learning; Long short term memory; Artificial intelligence; Accelerometer; Data mining; Artificial neural network","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004537212,0.0001323412,0.0001450032,0.0003690091,0.0002239525,0.0001854505,0.0002865811,0.00006829774,0.00003380266],"category_scores_gemma":[0.00003342663,0.0001314832,0.0001005805,0.001041582,0.00002504973,0.0002567362,0.0002187387,0.0001141928,0.0005267627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001479256,"about_ca_system_score_gemma":0.000060931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002065747,"about_ca_topic_score_gemma":0.0003494566,"domain_scores_codex":[0.9985322,0.00009849304,0.0002338283,0.0004419315,0.0004132467,0.0002803495],"domain_scores_gemma":[0.9992366,0.000152437,0.00005282709,0.0003363537,0.0001291037,0.00009272026],"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":[0.00001515615,0.0001909485,0.01363398,0.00005810943,0.0001036936,0.0001225531,0.0009463728,0.01200386,0.108349,0.0002371713,0.0003511597,0.863988],"study_design_scores_gemma":[0.0001900682,0.00003207226,0.04097225,0.0000343336,0.000007379749,0.0002177927,0.00007626685,0.9352158,0.02267509,0.00004033033,0.0002568289,0.0002818037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5752552,0.000009850572,0.4208836,0.00005280086,0.001244122,0.0000972839,0.000001117661,0.0007454993,0.001710549],"genre_scores_gemma":[0.9978325,0.00000118549,0.0006806302,0.00004919415,0.000173764,0.0000221436,0.000002060446,0.00001232581,0.001226225],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9232119,"threshold_uncertainty_score":0.6770645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06060572873750966,"score_gpt":0.2890058562656564,"score_spread":0.2284001275281468,"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."}}