{"id":"W2947764326","doi":"10.1186/s12984-019-0516-x","title":"Predicting wrist kinematics from motor unit discharge timings for the control of active prostheses","year":2019,"lang":"en","type":"article","venue":"Journal of NeuroEngineering and Rehabilitation","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"H2020 Marie Skłodowska-Curie Actions; Deutscher Akademischer Austauschdienst; Christian Doppler Forschungsgesellschaft; European Commission; Natural Sciences and Engineering Research Council of Canada; Bundesministerium für Wissenschaft, Forschung und Wirtschaft","keywords":"Wrist; Neural Prosthesis; Kinematics; Motor unit; Computer science; Electromyography; Degrees of freedom (physics and chemistry); Pattern recognition (psychology); Artificial intelligence; Simulation; Biomedical engineering; Physical medicine and rehabilitation; Engineering; Medicine; Surgery","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":[0.0004706591,0.0004510893,0.0002411449,0.0004736292,0.00008554687,0.0002639354,0.0001379553,0.0002605061,0.0008735686],"category_scores_gemma":[0.003158708,0.0001227492,0.0002169417,0.0003017638,0.0001181702,0.0002157313,0.0001663862,0.0002577849,0.0002221957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001525602,"about_ca_system_score_gemma":0.0002416239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001274978,"about_ca_topic_score_gemma":0.001415236,"domain_scores_codex":[0.9998811,0.000033414,0.00001299635,0.00002928379,0.00003250847,0.00001058766],"domain_scores_gemma":[0.9991192,0.0005768681,0.0001361493,0.00003618849,0.00009711539,0.0000345368],"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":[0.0008811396,0.0004433999,0.1592931,0.0003098057,0.0002217885,0.0002163626,0.0001959167,0.3242693,0.06631645,0.0005073712,0.0007895232,0.4465559],"study_design_scores_gemma":[0.00004405585,0.0005529962,0.1529969,0.0000353941,0.00006995104,0.0002776561,0.00005732917,0.8308589,0.01360463,0.001014173,0.0004660102,0.00002186437],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7952089,0.0005214124,0.202962,0.0001101662,0.00001736756,0.00004732296,0.000168688,0.0003336942,0.0006305361],"genre_scores_gemma":[0.9814577,0.0001061192,0.01801443,0.000008887415,0.0000106406,0.00002234624,0.0001281447,0.00000966604,0.0002420722],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001274978,"threshold_uncertainty_score":0.002922356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006288036359858641,"score_gpt":0.2054815918892375,"score_spread":0.1991935555293789,"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."}}