Tools for understanding and optimizing robotic gait training.

Tools for understanding and optimizing robotic gait training. Research Abstract Details 

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Tools for understanding and optimizing robotic gait training. Abstract Text:

David J Reinkensmeyer,Daisuke Aoyagi,Jeremy L Emken,Jose A Galvez,Wade Ichinose,Grigor Kerdanyan,Somboom Maneekobkunwong,Koyiro Minakata,Jeff A Nessler,Roger Weber,Roland R Roy,Ray de Leon,James E Bobrow,Susan J Harkema,V Reggie Edgerton,

This article reviews several tools we have developed to improve the understanding of locomotor training following spinal cord injury (SCI), with a view toward implementing locomotor training with robotic devices. We have developed (1) a small-scale robotic device that allows testing of locomotor training techniques in rodent models, (2) an instrumentation system that measures the forces and motions used by experienced human therapists as they manually assist leg movement during locomotor training, (3) a powerful, lightweight leg robot that allows investigation of motor adaptation during stepping in response to force-field perturbations, and (4) computational models for locomotor training. Results from the initial use of these tools suggest that an optimal gait-training robot will minimize disruptive sensory input, facilitate appropriate sensory input and gait mechanics, and intelligently grade and time its assistance. Currently, we are developing a pneumatic robot designed to meet these specifications as it assists leg and pelvic motion of people with SCI.

Tools for understanding and optimizing robotic gait training. Publishing Authors By Initials

DJ Reinkensmeyer,D Aoyagi,JL Emken,JA Galvez,W Ichinose,G Kerdanyan,S Maneekobkunwong,K Minakata,JA Nessler,R Weber,RR Roy,R de Leon,JE Bobrow,SJ Harkema,VR Edgerton,

For similar nervous system diseases: central nervous system diseases: spinal cord diseases: spinal cord injuries research abstracts see: nervous system diseases: central nervous system diseases: spinal cord diseases: spinal cord injuries research

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Tools for understanding and optimizing robotic gait training. Journal Published:

PUBLICATION TYPE: Review

Journal: Journal of rehabilitation research and development

VOLUME: 43

Page Numbers: 657-70

Journal Abbreviation:

ISSN: 0748-7711

DAY: 3

MONTH: 12

YEAR: 2007

Tools for understanding and optimizing robotic gait training. Information

Number of References: 42

LANGUAGE: eng

NlmUniqueID: 8410047

Tools for understanding and optimizing robotic gait training. Keywords Mesh Terms:

KEYWORDS: Spinal Cord Injuries

MESH TERMS: rehabilitation

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Grant and Affiliation Information for Tools for understanding and optimizing robotic gait training.

AFFILIATION: Department of Mechanical and Aerospace Engineering, University of California (UC) Irvine, Irvine, CA 92697-3975, USA. dreinken@uci.edu

Country: United States

AGENCY: United States NINDS

GRANT: R01NS42951

ACRONYM: NS

MEDLINETA: J Rehabil Res Dev

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