{"id":"W4402240792","doi":"10.1186/s12984-024-01447-1","title":"Human–exoskeleton interaction portrait","year":2024,"lang":"en","type":"article","venue":"Journal of NeuroEngineering and Rehabilitation","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Research Foundation","keywords":"Exoskeleton; Torque; Robot; Controller (irrigation); Kinematics; Human–robot interaction; Human–computer interaction; Computer science; Adaptation (eye); Wearable computer; Simulation; Artificial intelligence; Psychology","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":[0.0002326817,0.0003846142,0.0001991098,0.0005334853,0.0001370595,0.0004823391,0.000213814,0.0003457003,0.004416346],"category_scores_gemma":[0.0008629308,0.0001306984,0.0002620406,0.0002323319,0.0003619755,0.000473916,0.0004309006,0.0003102929,0.0005315279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001210814,"about_ca_system_score_gemma":0.0001125779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003920847,"about_ca_topic_score_gemma":0.0003059687,"domain_scores_codex":[0.9998395,0.00003227344,0.000008418604,0.00003581998,0.00006989829,0.00001398757],"domain_scores_gemma":[0.9996197,0.0001972251,0.00004558671,0.00005005775,0.00006591913,0.00002142568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0008187265,0.0002061682,0.01365261,0.0006247326,0.0001443179,0.001237538,0.001747724,0.1868324,0.5086855,0.01038186,0.005314916,0.2703536],"study_design_scores_gemma":[0.00006887852,0.0008280347,0.06689198,0.0001064448,0.00006873588,0.002175272,0.001039513,0.7873494,0.1131914,0.009312211,0.01886281,0.00010534],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3312647,0.0006136611,0.6457003,0.0003599083,0.0001064056,0.000113983,0.0007129387,0.001593425,0.01953469],"genre_scores_gemma":[0.96441,0.0003073247,0.03125809,0.00006580781,0.00002830051,0.0001318982,0.0003452652,0.0001225132,0.003330795],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004416346,"threshold_uncertainty_score":0.01477414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005109691483053153,"score_gpt":0.2333133296228546,"score_spread":0.2282036381398015,"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."}}