{"id":"W2989826115","doi":"10.1109/smc.2019.8914660","title":"Collision Detection for Human-Robot Interaction in an Industrial Setting using Force Myography and a Deep Learning Approach","year":2019,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Collision; Robot; Collision detection; Computer science; Collision avoidance; Artificial intelligence; Simulation; Artificial neural network; Industrial robot; Human–robot interaction; Human–computer interaction; Computer security","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":[0.0003832759,0.0004960636,0.000420281,0.000676901,0.0003303785,0.0003722455,0.000554005,0.0007951788,0.0009248357],"category_scores_gemma":[0.0007507682,0.0002778608,0.0003827718,0.0003451038,0.0002463545,0.0005163621,0.0007513103,0.000515071,0.0001661205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003976329,"about_ca_system_score_gemma":0.0004899096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003050453,"about_ca_topic_score_gemma":0.00372002,"domain_scores_codex":[0.9997559,0.0000413107,0.0000142591,0.00006152424,0.00007689129,0.00005004581],"domain_scores_gemma":[0.9997678,0.00007867978,0.00004890564,0.00001811622,0.00005771199,0.00002877267],"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":[0.0007110518,0.0004376208,0.01610055,0.0001900551,0.0001692569,0.0007793611,0.0004487739,0.1741224,0.07741834,0.001969085,0.001807614,0.7258459],"study_design_scores_gemma":[0.000007985842,0.0001063206,0.004195075,0.000007099247,0.00001468527,0.0001248443,0.00004332179,0.9895157,0.004926073,0.0007362444,0.0003126508,0.00001008072],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1561801,0.0004853561,0.8413572,0.0001720326,0.00004303621,0.00005913242,0.00003855718,0.0005517599,0.001112885],"genre_scores_gemma":[0.9027737,0.0001923907,0.0948495,0.0000885618,0.00002532097,0.00005257622,0.0000612713,0.00001812735,0.001938541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003050453,"threshold_uncertainty_score":0.006065369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05125701469287983,"score_gpt":0.2845671634226251,"score_spread":0.2333101487297453,"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."}}