{"id":"W2943251243","doi":"10.1109/access.2019.2907925","title":"CapsFall: Fall Detection Using Ultra-Wideband Radar and Capsule Network","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Computer science; Radar; Artificial intelligence; Multilayer perceptron; Feature extraction; Convolutional neural network; Decision tree; Feature (linguistics); Support vector machine; Perceptron; Feature learning; Machine learning; Pattern recognition (psychology); Artificial neural network; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004279267,0.0009306853,0.000606108,0.001144688,0.0002240604,0.0004399234,0.000761093,0.0007919073,0.00125613],"category_scores_gemma":[0.001241582,0.0002386921,0.0004355781,0.0008743711,0.0002987962,0.001051985,0.000967767,0.0006520079,0.0005342718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003024231,"about_ca_system_score_gemma":0.0004526911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001889845,"about_ca_topic_score_gemma":0.003538446,"domain_scores_codex":[0.9996265,0.00005975865,0.000018022,0.00008983292,0.0001615004,0.00004435125],"domain_scores_gemma":[0.9997059,0.00007090286,0.00006958833,0.00003391215,0.00009083354,0.00002885557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006993844,0.0003693975,0.01472308,0.0003014533,0.0001852876,0.0005491937,0.0001134893,0.03749767,0.06443687,0.002374139,0.010322,0.8684281],"study_design_scores_gemma":[0.00005054327,0.0003815506,0.01448932,0.00004707853,0.00009309068,0.001231098,0.00006441602,0.9483699,0.02788391,0.00236866,0.004968519,0.0000518761],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05749775,0.000772344,0.9369929,0.0002376952,0.0001501175,0.0001199571,0.0003027749,0.001953206,0.00197318],"genre_scores_gemma":[0.5972905,0.001178526,0.3934661,0.0004595301,0.000185386,0.0002328366,0.001403839,0.0001218561,0.005661294],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001889845,"threshold_uncertainty_score":0.004202187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01625577116891394,"score_gpt":0.2388492783330613,"score_spread":0.2225935071641474,"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."}}