{"id":"W3093502284","doi":"10.1109/biorob49111.2020.9224364","title":"Comparative Analysis of Environment Recognition Systems for Control of Lower-Limb Exoskeletons and Prostheses","year":2020,"lang":"en","type":"article","venue":"","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Exoskeleton; Computer science; Convolutional neural network; Artificial intelligence; Wearable computer; Contextual image classification; Deep learning; Machine learning; Pattern recognition (psychology); Metric (unit); Artificial neural network; Image (mathematics); Simulation; Embedded system; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005081507,0.00005488957,0.0002614915,0.00004978463,0.00001036857,0.000004580839,0.00002346975,0.00002530588,0.00001292594],"category_scores_gemma":[0.00001368227,0.00004571701,0.00006388778,0.00009010181,0.00004005953,0.00001993494,0.000004548877,0.0000194168,9.432255e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006895324,"about_ca_system_score_gemma":0.00000287673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002879043,"about_ca_topic_score_gemma":0.000001012052,"domain_scores_codex":[0.9996044,0.00001220037,0.0002041938,0.00006949565,0.00005723732,0.00005245019],"domain_scores_gemma":[0.9996839,0.0001518967,0.00004187044,0.00005275964,0.00003504542,0.00003459567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007299324,0.00006905153,0.0007810234,0.0004532904,0.001087823,1.418536e-7,0.001084102,0.9712068,0.02333198,0.001239151,0.00008383317,0.0005898298],"study_design_scores_gemma":[0.0003471247,0.0004975614,0.001358112,0.00001196123,0.0004143457,1.150255e-7,0.0005016007,0.9929695,0.003489849,0.00004344193,0.0002945411,0.00007185266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4792163,0.0007522689,0.5179579,0.0002466883,0.00004632352,0.0009291258,0.0003504397,0.00003821398,0.0004627162],"genre_scores_gemma":[0.9979663,0.00006507596,0.001895536,0.00001044296,0.000005633839,0.00002976953,0.00001490425,0.000004224327,0.000008146996],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.51875,"threshold_uncertainty_score":0.1864285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02653625115272647,"score_gpt":0.2283234036923631,"score_spread":0.2017871525396366,"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."}}