{"id":"W2954663339","doi":"10.1109/lra.2019.2924852","title":"Design and Control of a Multifunctional Ankle Exoskeleton Powered by Magnetorheological Actuators to Assist Walking, Jumping, and Landing","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"Theratechnologies (Canada); Institut interdisciplinaire d'innovation technologique","funders":"","keywords":"Exoskeleton; Jumping; Magnetorheological fluid; Clutch; Torque; Actuator; Ankle; Powered exoskeleton; Simulation; Computer science; Engineering; Control theory (sociology); Automotive engineering; Control engineering; Damper; Artificial intelligence; Control (management); Physics","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":[0.0001780108,0.0001284977,0.0001891194,0.00009202121,0.00005027534,0.00004897627,0.00003707031,0.0000732507,0.00001010751],"category_scores_gemma":[0.00002327681,0.0001154002,0.00002158121,0.00006809593,0.00005456146,0.00007635866,0.00001315356,0.00007563071,0.000003311388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002326015,"about_ca_system_score_gemma":0.000004748067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003755798,"about_ca_topic_score_gemma":4.799057e-7,"domain_scores_codex":[0.9993106,0.00003442896,0.0002203403,0.0001657617,0.0001233399,0.0001455938],"domain_scores_gemma":[0.9995763,0.0001819723,0.0000520037,0.00007900403,0.00002823058,0.00008246135],"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":[0.00003497178,0.0000456993,0.01965323,0.0002980894,0.00009144381,0.000001890515,0.0008398809,0.6594232,0.3098727,0.001057031,0.001638027,0.007043844],"study_design_scores_gemma":[0.001069102,0.0001886943,0.02913358,0.00006450313,0.00002984384,0.000007863519,0.00004181072,0.9676852,0.00110705,0.0001101337,0.0003196376,0.0002425745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4846394,0.00009537343,0.5141802,0.0006263187,0.0001768264,0.0002184238,0.000005907482,0.00004808383,0.000009369642],"genre_scores_gemma":[0.9786404,0.00002828478,0.02097278,0.0002905586,0.00001786318,0.00000722459,0.000006470702,0.00001495482,0.00002141982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.494001,"threshold_uncertainty_score":0.4705884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005397410413065643,"score_gpt":0.1962229358067784,"score_spread":0.1908255253937127,"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."}}