Literature DB >> 25350933

Restoration of gait for spinal cord injury patients using HAL with intention estimator for preferable swing speed.

Atsushi Tsukahara, Yasuhisa Hasegawa, Kiyoshi Eguchi, Yoshiyuki Sankai.   

Abstract

This paper proposes a novel gait intention estimator for an exoskeleton-wearer who needs gait support owing to walking impairment. The gait intention estimator not only detects the intention related to the start of the swing leg based on the behavior of the center of ground reaction force (CoGRF), but also infers the swing speed depending on the walking velocity. The preliminary experiments categorized into two stages were performed on a mannequin equipped with the exoskeleton robot [Hybrid Assistive Limb: (HAL)] including the proposed estimator. The first experiment verified that the gait support system allowed the mannequin to walk properly and safely. In the second experiment, we confirmed the differences in gait characteristics attributed to the presence or absence of the proposed swing speed profile. As a feasibility study, we evaluated the walking capability of a severe spinal cord injury patient supported by the system during a 10-m walk test. The results showed that the system enabled the patient to accomplish a symmetrical walk from both spatial and temporal standpoints while adjusting the speed of the swing leg. Furthermore, the critical differences of gait between our system and a knee-ankle-foot orthosis were obtained from the CoGRF distribution and the walking time. Through the tests, we demonstrated the effectiveness and practical feasibility of the gait support algorithms.

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Mesh:

Year:  2014        PMID: 25350933     DOI: 10.1109/TNSRE.2014.2364618

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  9 in total

1.  A convolutional neural network for steady state visual evoked potential classification under ambulatory environment.

Authors:  No-Sang Kwak; Klaus-Robert Müller; Seong-Whan Lee
Journal:  PLoS One       Date:  2017-02-22       Impact factor: 3.240

2.  A wearable exoskeleton suit for motion assistance to paralysed patients.

Authors:  Bing Chen; Chun-Hao Zhong; Xuan Zhao; Hao Ma; Xiao Guan; Xi Li; Feng-Yan Liang; Jack Chun Yiu Cheng; Ling Qin; Sheung-Wai Law; Wei-Hsin Liao
Journal:  J Orthop Translat       Date:  2017-03-23       Impact factor: 5.191

3.  Dynamic Balance Gait for Walking Assistance Exoskeleton.

Authors:  Qiming Chen; Hong Cheng; Chunfeng Yue; Rui Huang; Hongliang Guo
Journal:  Appl Bionics Biomech       Date:  2018-07-02       Impact factor: 1.781

4.  Systematic review on wearable lower-limb exoskeletons for gait training in neuromuscular impairments.

Authors:  Antonio Rodríguez-Fernández; Joan Lobo-Prat; Josep M Font-Llagunes
Journal:  J Neuroeng Rehabil       Date:  2021-02-01       Impact factor: 4.262

5.  Preliminary Study on a Novel Protocol for Improving Familiarity with a Lower-Limb Robotic Exoskeleton in Able-Bodied, First-Time Users.

Authors:  Jan C L Lau; Katja Mombaur
Journal:  Front Robot AI       Date:  2022-01-10

6.  Effects of Individualized Gait Rehabilitation Robotics for Gait Training on Hemiplegic Patients: Before-After Study in the Same Person.

Authors:  Zhao Guo; Jing Ye; Shisheng Zhang; Lanshuai Xu; Gong Chen; Xiao Guan; Yongqiang Li; Zhimian Zhang
Journal:  Front Neurorobot       Date:  2022-03-08       Impact factor: 2.650

7.  Hip-Knee Coupling Exoskeleton With Offset Theory for Walking Assistance.

Authors:  Jianfeng Ma; Decheng Sun; Xiao Chen
Journal:  Front Bioeng Biotechnol       Date:  2022-01-31

8.  Cybernic treatment with wearable cyborg Hybrid Assistive Limb (HAL) improves ambulatory function in patients with slowly progressive rare neuromuscular diseases: a multicentre, randomised, controlled crossover trial for efficacy and safety (NCY-3001).

Authors:  Takashi Nakajima; Yoshiyuki Sankai; Shinjiro Takata; Yoko Kobayashi; Yoshihito Ando; Masanori Nakagawa; Toshio Saito; Kayoko Saito; Chiho Ishida; Akira Tamaoka; Takako Saotome; Tetsuo Ikai; Hisako Endo; Kazuhiro Ishii; Mitsuya Morita; Takashi Maeno; Kiyonobu Komai; Tetsuhiko Ikeda; Yuka Ishikawa; Shinichiro Maeshima; Masashi Aoki; Michiya Ito; Tatsuya Mima; Toshihiko Miura; Jun Matsuda; Yumiko Kawaguchi; Tomohiro Hayashi; Masahiro Shingu; Hiroaki Kawamoto
Journal:  Orphanet J Rare Dis       Date:  2021-07-07       Impact factor: 4.123

9.  Factors Predicting the Effects of Hybrid Assistive Limb Robot Suit during the Acute Phase of Central Nervous System Injury.

Authors:  Hideo Chihara; Yasushi Takagi; Kazunari Nishino; Kazumichi Yoshida; Yoshiki Arakawa; Takayuki Kikuchi; Yohei Takenobu; Susumu Miyamoto
Journal:  Neurol Med Chir (Tokyo)       Date:  2015-11-05       Impact factor: 1.742

  9 in total

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