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Symmetry
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Biomechanics and Energetics of Human Motion When Walking with Lower...
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Biomechanics and Energetics of Human Motion When Walking with Lower Limb Orthoses, Prosthesis, and Exoskeletons

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Life Sciences".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 3330

Special Issue Editor

Dr. Fei Gao

E-Mail Website
Guest Editor
Associate Professor, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Interests: lower limb prosthesis; human motion identification; application of smart materials; energy harvesting from human motion

Special Issue Information

Dear Colleagues,

Lower limb assistance devices, including prostheses, orthoses, and exoskeletons, are developed to help subjects with impaired locomotion function to restore mobility. In the last decade, powered assistance devices have attracted substantial attention from academics and industries, as these devices can better aid users in regaining a more natural gait compared to traditional passive devices. In addition, it is reported that powered transtibial and transfemoral prostheses can improve users’ self-selected walking speed, decrease the metabolic cost of the process, and reduce impact force on the sound side.

As a universal human activity, walking is considered an important research area. Many research papers have demonstrated that the able-bodied always have a symmetrical gait. However, stroke patients and amputees often experience asymmetrical behavior of the lower extremities, significantly increasing the load of the sound side or the dominated side, decreasing the walking speed, and requiring greater human effort in walking.

This Special Issue will present studies considering the symmetry, biomechanics, and energetics of human motion with lower limb orthoses, prostheses, and exoskeletons.

Dr. Fei Gao
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • gait study
  • human motion
  • symmetry
  • metabolic cost
  • lower limb prosthesis
  • orthosis
  • lower limb exoskeleton

Published Papers (2 papers)

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Research

19 pages, 724 KiB  
Article
Event-Triggered Sliding Mode Impulsive Control for Lower Limb Rehabilitation Exoskeleton Robot Gait Tracking
by Yang Liu, Shiguo Peng, Jiajun Zhang, Kan Xie, Zhuoyi Lin and Wei-Hsin Liao
Symmetry 2023, 15(1), 224; https://doi.org/10.3390/sym15010224 - 12 Jan 2023
Cited by 1 | Viewed by 1510
Abstract
Lower limb rehabilitation exoskeleton robots (LLRERs) play an important role in lower limb rehabilitation training and assistance walking for patients with lower limb movement disorders. In order to reduce and eliminate adverse effects on the accuracy of human motion gait tracking during walking [...] Read more.
Lower limb rehabilitation exoskeleton robots (LLRERs) play an important role in lower limb rehabilitation training and assistance walking for patients with lower limb movement disorders. In order to reduce and eliminate adverse effects on the accuracy of human motion gait tracking during walking with an LLRER, which is caused by the gravity and friction, the periodic ground shock force, and the human–exoskeleton interaction force, this paper proposes a feedforward–feedback hybrid control strategy of sliding mode impulsive control with gravity and friction compensation, based on the event-triggered mechanism of Lyapunov function. Firstly, to realize high-precision gait tracking with bounded error, some constraints on controller parameters are deduced by analyzing the Lyapunov-based stability. Secondly, the Zeno behavior of impulsive event triggers is excluded by the analysis of three different cases of the triggering time sequence. Finally, the effectiveness of the proposed hybrid controller is verified by the numerical simulation of the LLRER human–exoskeleton integrated system based on a three-link simplified model. It shows that an event-triggered sliding mode impulsive control strategy with gravity and friction compensation can achieve complete gait tracking with bounded error and has excellent dynamic performance under the constraints. Full article
(This article belongs to the Special Issue Biomechanics and Energetics of Human Motion When Walking with Lower Limb Orthoses, Prosthesis, and Exoskeletons)
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16 pages, 4888 KiB  
Article
Gait Phase Classification of Lower Limb Exoskeleton Based on a Compound Network Model
by Yuxuan Xia, Jiaqian Li, Dong Yang and Wei Wei
Symmetry 2023, 15(1), 163; https://doi.org/10.3390/sym15010163 - 05 Jan 2023
Cited by 3 | Viewed by 1284
Abstract
The classification of lower limb gait phase is very important for the control of exoskeleton robots. In order to enable the exoskeleton to determine gait phase and provide appropriate assistance to the wearer, we propose a compound network based on CNN-BiLSTM. The method [...] Read more.
The classification of lower limb gait phase is very important for the control of exoskeleton robots. In order to enable the exoskeleton to determine gait phase and provide appropriate assistance to the wearer, we propose a compound network based on CNN-BiLSTM. The method uses data from inertial measurement units placed on the leg and pressure sensor arrays placed on the sole as inputs to the model. The convolutional neural network (CNN) is used to obtain the local key features of gait data, and then the bidirectional long short-term memory (BiLSTM) network is used to extract the serialized gait phase information from the local key features to obtain the high-level feature expression. Finally, the seven phases of both feet were obtained through the classification of the softmax layer. We designed a gait acquisition system and collected the gait data from seven subjects at varying walking speeds. In the test set, the highest gait phase classification accuracy can reach 95.09%. We compared the proposed model with the long short-term memory (LSTM) network and gated recurrent unit (GRU) network. The experimental results show that the average accuracy of CNN-BiLSTM network from seven subjects is 0.417% higher than that of the LSTM network and 0.596% higher than that of the GRU network. Therefore, the ability of the CNN-BiLSTM network to classify gait phases can be applied in designing exoskeleton controllers that can better assist for different gait phases correctly to assist the wearer to walk. Full article
(This article belongs to the Special Issue Biomechanics and Energetics of Human Motion When Walking with Lower Limb Orthoses, Prosthesis, and Exoskeletons)
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