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A Wearable Hip Assist Robot Can Improve Gait Function and Cardiopulmonary Metabolic Efficiency in Elderly Adults

The aims of this paper were to investigate the effectiveness of a newly developed wearable hip assist robot, that uses an active assist algorithm to improve gait function, muscle effort, and cardiopulmonary metabolic efficiency in elderly adults. Thirty elderly adults (15 males/ 15 females) particip...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering 2017-09, Vol.25 (9), p.1549-1557
Main Authors: Lee, Hwang-Jae, Lee, Suhyun, Chang, Won Hyuk, Seo, Keehong, Shim, Youngbo, Choi, Byung-Ok, Ryu, Gyu-Ha, Kim, Yun-Hee
Format: Article
Language:English
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Summary:The aims of this paper were to investigate the effectiveness of a newly developed wearable hip assist robot, that uses an active assist algorithm to improve gait function, muscle effort, and cardiopulmonary metabolic efficiency in elderly adults. Thirty elderly adults (15 males/ 15 females) participated in thispaper. The experimental protocol consisted of overground gait at comfortable speed under three different conditions: free gait without robot assistance, robot-assisted gait with zero torque (RAG-Z), and full RAG. Under all conditions, muscle effort was analyzed using a 12-channel surface electromyography system. Spatio-temporal data were collected at 120 Hz using a 3-D motion capture system with six infrared cameras. Metabolic cost parameters were collected as oxygen consumption per unit (ml/min/kg) and aerobic energy expenditure (Kcal/min). In the RAG condition, participants demonstrated improved gait function, decreased muscle effort, and reduced metabolic cost. Although the hip assist robot only provides assistance at the hip joint, our results demonstrated a clear reduction in knee and ankle muscle activity in addition to decreased hip flexor and extensor activity. Our findings suggest that this robot has the potential to improve stabilization of the trunk during walking in elderly adults.
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2017.2664801