{"id":"W4367628093","doi":"10.3390/robotics12030066","title":"Human–Exoskeleton Interaction Force Estimation in Indego Exoskeleton","year":2023,"lang":"en","type":"article","venue":"Robotics","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Toronto Rehabilitation Institute; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Exoskeleton; Torque; Control theory (sociology); Artificial neural network; Robot; Work (physics); Computer science; Engineering; Dynamics (music); Simulation; Control engineering; Artificial intelligence; Physics; Control (management); Mechanical engineering; Acoustics","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":[0.0003669091,0.000646166,0.000403039,0.0004085809,0.0002540823,0.0003214653,0.0002880311,0.0005161808,0.0009889511],"category_scores_gemma":[0.0007872514,0.0002350343,0.0002610967,0.0001845528,0.0001746424,0.000380844,0.0004158387,0.0002975511,0.0001731694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001272559,"about_ca_system_score_gemma":0.0002609282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002059396,"about_ca_topic_score_gemma":0.002909554,"domain_scores_codex":[0.9998668,0.00002399959,0.00001028532,0.00003932366,0.0000425697,0.00001703924],"domain_scores_gemma":[0.9998382,0.00006724923,0.00003359165,0.0000171738,0.00003446646,0.000009304059],"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":[0.0006712099,0.0002809135,0.01450516,0.0004636452,0.0001861691,0.0008168647,0.0003946293,0.3592157,0.160582,0.001226563,0.001596995,0.4600602],"study_design_scores_gemma":[0.00001386519,0.0001285821,0.01499974,0.0000294036,0.00002620171,0.0002497198,0.00006338435,0.9620829,0.02063361,0.0006907042,0.001059982,0.00002180816],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2352735,0.0005602561,0.7610505,0.0001452406,0.00005115018,0.00006291088,0.00009173735,0.0007070397,0.002057709],"genre_scores_gemma":[0.9387651,0.000153654,0.05941129,0.00004365388,0.00001108672,0.00005371454,0.00009126727,0.00002519097,0.001445141],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002059396,"threshold_uncertainty_score":0.004094839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01467035942234946,"score_gpt":0.2707592079442072,"score_spread":0.2560888485218578,"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."}}