{"id":"W3175424034","doi":"10.1101/2021.06.21.449311","title":"Online Reference Trajectory Adaptation: A Personalized Control Strategy for Lower Limb Exoskeletons","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Alberta; Toronto Rehabilitation Institute; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Exoskeleton; Trajectory; Adaptation (eye); Convergence (economics); Computer science; Gait; Control theory (sociology); Simulation; Artificial intelligence; Physical medicine and rehabilitation; Control (management); 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003028454,0.0005846348,0.0006950156,0.0001938834,0.0001297029,0.0002417671,0.0003897319,0.0006334944,0.00007995115],"category_scores_gemma":[0.0001848548,0.0006474793,0.0003201452,0.0002743143,0.0001316153,0.0001160008,0.00007698397,0.0007751402,0.00001162247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002620554,"about_ca_system_score_gemma":0.0007003119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008897744,"about_ca_topic_score_gemma":0.00001133513,"domain_scores_codex":[0.9976635,0.00008996094,0.0006480061,0.0007328494,0.0003274128,0.0005382492],"domain_scores_gemma":[0.9975929,0.0002208712,0.0001816518,0.0008712371,0.0008663643,0.000267004],"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":[0.0002188277,0.001240929,0.0001902353,0.004389285,0.001345267,0.0001213511,0.0001902646,0.5393358,0.4451747,0.006831677,0.0009260844,0.0000355868],"study_design_scores_gemma":[0.009353364,0.001334251,0.03283622,0.002460803,0.001377867,1.988876e-7,0.0003760488,0.8952382,0.02312488,0.00003894336,0.02761418,0.006245076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7193682,0.007394905,0.2621677,0.0003820951,0.003611035,0.002602316,0.002968383,0.001474728,0.00003069895],"genre_scores_gemma":[0.9716495,0.0004351481,0.02677647,0.0001158151,0.0003688737,0.000442171,0.000006472141,0.0001906625,0.00001492533],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4220498,"threshold_uncertainty_score":0.9995977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02668436573549198,"score_gpt":0.2344769247367712,"score_spread":0.2077925590012792,"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."}}