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When deployed on our hip exoskeleton, the TCN achieved an average root mean square error of 0.142 newton-meters per kilogram across 35 ambulatory conditions without any user-specific calibration. Further, the unified controller significantly reduced user metabolic cost and lower-limb positive work during level-ground and incline walking compared with walking without wearing the exoskeleton. This advancement bridges the gap between in-lab exoskeleton technology and real-world human ambulation, making exoskeleton control technology viable for a broad community.<\/jats:p>","DOI":"10.1126\/scirobotics.adi8852","type":"journal-article","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T17:58:17Z","timestamp":1710957497000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":99,"title":["Estimating human joint moments unifies exoskeleton control, reducing user effort"],"prefix":"10.1126","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9725-854X","authenticated-orcid":true,"given":"Dean D.","family":"Molinaro","sequence":"first","affiliation":[{"name":"George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA."},{"name":"Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA 30332, USA."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5846-1550","authenticated-orcid":true,"given":"Inseung","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA."}]},{"given":"Aaron J.","family":"Young","sequence":"additional","affiliation":[{"name":"George W. 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