{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000982846,0.0001779878,0.0001831244,0.0000673829,0.00007980464,0.0001583535,0.0001749349,0.00009758119,0.00001320096],"category_scores_gemma":[0.00001037811,0.0001896978,0.00003535806,0.0002256696,0.00001995615,0.0006085818,0.00002637968,0.0001760635,0.00002801265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009805879,"about_ca_system_score_gemma":0.000009741002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005048708,"about_ca_topic_score_gemma":0.0001618953,"domain_scores_codex":[0.9991072,0.00001918987,0.0001713723,0.0002193048,0.0001421296,0.0003408269],"domain_scores_gemma":[0.999592,0.00005209406,0.00003430237,0.0002027854,0.00003079185,0.00008803891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005249732,0.000004028921,0.03719677,0.00008821338,0.00003447916,0.000008070852,0.00006263003,0.05683758,0.9039339,0.000004450534,0.00009695529,0.001727615],"study_design_scores_gemma":[0.0005249652,0.00003098226,0.005250131,0.000119284,0.0000307103,0.00003112769,0.00002661899,0.00291978,0.9898056,0.000342095,0.0004813588,0.0004373065],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9705877,0.0005297484,0.024856,0.000002978883,0.002455648,0.0002170153,0.000002841851,0.0002021216,0.001145957],"genre_scores_gemma":[0.9987803,0.00005386893,0.0003258692,0.0000218606,0.0007223512,0.000007193814,0.000001157697,0.00005727687,0.00003011627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08587168,"threshold_uncertainty_score":0.7735652,"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."}}