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Design and Application of Wearable and Rehabilitation Robotics

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: closed (10 February 2024) | Viewed by 4920

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Special Issue Editors

Dr. Arash Arami

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Guest Editor

Mechanical and Mechatronics Engineering, University of Waterloo, University Avenue West, Waterloo, ON N2L 3G1, Canada
Interests: assistive robotics; human-robot interaction; rehabilitation engineering; neuromechanics and sensorimotor modelling; neural control of movements; intelligent systems; wearable systems; system identification

Prof. Dr. Katja Mombaur

E-Mail Website
Guest Editor

Department of Systems Design Engineering, University of Waterloo, University Avenue West, Waterloo, ON N2L 3G1, Canada
Interests: humanoid robots; wearable robots and assistive devices; human movement; optimal control

Prof. Dr. John McPhee

E-Mail Website
Guest Editor

Department of Systems Design Engineering, University of Waterloo, University Avenue West, Waterloo, ON N2L 3G1, Canada
Interests: exoskeletons; rehabilitation robots; machine learning; dynamics; model-based control; biomechatronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Each year millions of people lose their ability to move due to stroke, amputation, aging, and debilitating neurological conditions such as spinal cord injury, cerebral palsy, multiple sclerosis, and Parkinson’s disease. Recent advancements in robotics have provided new solutions in the form of robotic upper and lower limb rehabilitation systems, limb prosthetics, exoskeletons, and exosuits. Such robotic systems significantly impact the rehabilitation field by delivering high-dose exercises to those with movement deficits. In addition, wearable robots enable many users to increase their mobility and effectively perform activities of daily living. Despite several challenges facing wearable and rehabilitation robotics, recent advances in artificial intelligence (AI), sensors and actuators, the development of lightweight materials, and our improved understanding of neuromechanics of movement have opened new pathways that can address those challenges, e.g. optimizing human-robot interactions when using wearable and rehabilitation robotics.

The goal of this special issue is to present cutting-edge research in the field of assistive and rehabilitation robotics. Contributions are sought in all areas relevant to assistive and rehabilitation robotics, including but not limited to:

Investigations into human-robot interaction and movement neuromechanics
Robotic neurorehabilitation
Human augmentation with wearable robotics
Development of novel assistive and rehabilitation robotic systems
Advanced techniques in control of wearable robotic systems
Human-wearable robot shared control
AI in modeling and control of human-wearable robot interaction
Human-in-the-loop optimization of assistance
Neural interfaces for assistive and rehabilitation robotics

Dr. Arash Arami
Prof. Dr. Katja Mombaur
Prof. Dr. John McPhee
Guest Editors

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Keywords

wearable robots
exoskeletons
prosthetics
neurorehabilitation
neuromechanics
sensorimotor augmentation
human-robot interaction
artificial intelligence
shared control

Published Papers (6 papers)

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Research

18 pages, 2344 KiB

Open AccessArticle

IMU-Based Real-Time Estimation of Gait Phase Using Multi-Resolution Neural Networks

by Lyndon Tang, Mohammad Shushtari and Arash Arami

Sensors 2024, 24(8), 2390; https://doi.org/10.3390/s24082390 - 9 Apr 2024

Viewed by 463

Abstract

This work presents a real-time gait phase estimator using thigh- and shank-mounted inertial measurement units (IMUs). A multi-rate convolutional neural network (CNN) was trained to estimate gait phase for a dataset of 16 participants walking on an instrumented treadmill with speeds varying between 0.1 to 1.9 m/s, and conditions such as asymmetric walking, stop–start, and sudden speed changes. One-subject-out cross-validation was used to assess the robustness of the estimator to the gait patterns of new individuals. The proposed model had a spatial root mean square error of

5.00 \pm 1.65 %

, and a temporal mean absolute error of

2.78 \pm 0.97 %

evaluated at the heel strike. A second cross-validation was performed to show that leaving out any of the walking conditions from the training dataset did not result in significant performance degradation. A 2-sample Kolmogorov–Smirnov test showed that there was no significant increase in spatial or temporal error when testing on the abnormal walking conditions left out of the training set. The results of the two cross-validations demonstrate that the proposed model generalizes well across new participants, various walking speeds, and gait patterns, showcasing its potential for use in investigating patient populations with pathological gaits and facilitating robot-assisted walking. Full article

(This article belongs to the Special Issue Design and Application of Wearable and Rehabilitation Robotics)

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22 pages, 10000 KiB

Open AccessArticle

A Multistage Hemiplegic Lower-Limb Rehabilitation Robot: Design and Gait Trajectory Planning

by Xincheng Wang, Hongbo Wang, Bo Zhang, Desheng Zheng, Hongfei Yu, Bo Cheng and Jianye Niu

Sensors 2024, 24(7), 2310; https://doi.org/10.3390/s24072310 - 5 Apr 2024

Viewed by 554

Abstract

Most lower limb rehabilitation robots are limited to specific training postures to adapt to stroke patients in multiple stages of recovery. In addition, there is a lack of attention to the switching functions of the training side, including left, right, and bilateral, which enables patients with hemiplegia to rehabilitate with a single device. This article presents an exoskeleton robot named the multistage hemiplegic lower-limb rehabilitation robot, which has been designed to do rehabilitation in multiple training postures and training sides. The mechanism consisting of the thigh, calf, and foot is introduced. Additionally, the design of the multi-mode limit of the hip, knee, and ankle joints supports delivering therapy in any posture and training sides to aid patients with hemiplegia in all stages of recovery. The gait trajectory is planned by extracting the gait motion trajectory model collected by the motion capture device. In addition, a control system for the training module based on adaptive iterative learning has been simulated, and its high-precision tracking performance has been verified. The gait trajectory experiment is carried out, and the results verify that the trajectory tracking performance of the robot has good performance. Full article

(This article belongs to the Special Issue Design and Application of Wearable and Rehabilitation Robotics)

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19 pages, 8782 KiB

Open AccessArticle

Patient’s Healthy-Limb Motion Characteristic-Based Assist-As-Needed Control Strategy for Upper-Limb Rehabilitation Robots

by Bingjing Guo, Zhenzhu Li, Mingxiang Huang, Xiangpan Li and Jianhai Han

Sensors 2024, 24(7), 2082; https://doi.org/10.3390/s24072082 - 25 Mar 2024

Viewed by 460

Abstract

The implementation of a progressive rehabilitation training model to promote patients’ motivation efforts can greatly restore damaged central nervous system function in patients. Patients’ active engagement can be effectively stimulated by assist-as-needed (AAN) robot rehabilitation training. However, its application in robotic therapy has been hindered by a simple determination method of robot-assisted torque which focuses on the evaluation of only the affected limb’s movement ability. Moreover, the expected effect of assistance depends on the designer and deviates from the patient’s expectations, and its applicability to different patients is deficient. In this study, we propose a control method with personalized treatment features based on the idea of estimating and mapping the stiffness of the patient’s healthy limb. This control method comprises an interactive control module in the task-oriented space based on the quantitative evaluation of motion needs and an inner-loop position control module for the pneumatic swing cylinder in the joint space. An upper-limb endpoint stiffness estimation model was constructed, and a parameter identification algorithm was designed. The upper limb endpoint stiffness which characterizes the patient’s ability to complete training movements was obtained by collecting surface electromyographic (sEMG) signals and human–robot interaction forces during patient movement. Then, the motor needs of the affected limb when completing the same movement were quantified based on the performance of the healthy limb. A stiffness-mapping algorithm was designed to dynamically adjust the rehabilitation training trajectory and auxiliary force of the robot based on the actual movement ability of the affected limb, achieving AAN control. Experimental studies were conducted on a self-developed pneumatic upper limb rehabilitation robot, and the results showed that the proposed AAN control method could effectively estimate the patient’s movement needs and achieve progressive rehabilitation training. This rehabilitation training robot that simulates the movement characteristics of the patient’s healthy limb drives the affected limb, making the intensity of the rehabilitation training task more in line with the patient’s pre-morbid limb-use habits and also beneficial for the consistency of bilateral limb movements. Full article

(This article belongs to the Special Issue Design and Application of Wearable and Rehabilitation Robotics)

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14 pages, 1308 KiB

Open AccessArticle

Experimental Study of Fully Passive, Fully Active, and Active–Passive Upper-Limb Exoskeleton Efficiency: An Assessment of Lifting Tasks

by Ali Nasr, Clark R. Dickerson and John McPhee

Sensors 2024, 24(1), 63; https://doi.org/10.3390/s24010063 - 22 Dec 2023

Viewed by 855

Abstract

Recently, robotic exoskeletons are gaining attention for assisting industrial workers. The exoskeleton power source ranges from fully passive (FP) to fully active (FA), or a mixture of both. The objective of this experimental study was to assess the efficiency of a new active–passive (AP) shoulder exoskeleton using statistical analyses of 11 quantitative measures from surface electromyography (sEMG) and kinematic data and a user survey for weight lifting tasks. Two groups of females and males lifted heavy kettlebells, while a shoulder exoskeleton helped them in modes of fully passive (FP), fully active (FA), and active–passive (AP). The AP exoskeleton outperformed the FP and FA exoskeletons because the participants could hold the weighted object for nearly twice as long before fatigue occurred. Future developments should concentrate on developing sex-specific controllers as well as on better-fitting wearable devices for women. Full article

(This article belongs to the Special Issue Design and Application of Wearable and Rehabilitation Robotics)

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13 pages, 6944 KiB

Open AccessArticle

Effect of Robotic-Assisted Gait at Different Levels of Guidance and Body Weight Support on Lower Limb Joint Kinematics and Coordination

by Yosra Cherni, Yoann Blache, Mickael Begon, Laurent Ballaz and Fabien Dal Maso

Sensors 2023, 23(21), 8800; https://doi.org/10.3390/s23218800 - 29 Oct 2023

Viewed by 1011

Abstract

The Lokomat provides task-oriented therapy for patients with gait disorders. This robotic technology drives the lower limbs in the sagittal plane. However, normative gait also involves motions in the coronal and transverse planes. This study aimed to compare the Lokomat with Treadmill gait through three-dimensional (3D)-joint kinematics and inter-joint coordination. Lower limb kinematics was recorded in 18 healthy participants who walked at 3 km/h on a Treadmill or in a Lokomat with nine combinations of Guidance (30%, 50%, 70%) and bodyweight support (30%, 50%, 70%). Compared to the Treadmill, the Lokomat altered pelvic rotation, decreased pelvis obliquity and hip adduction, and increased ankle rotation. Moreover, the Lokomat resulted in significantly slower velocity at the hip, knee, and ankle flexion compared to the treadmill condition. Moderate to strong correlations were observed between the Treadmill and Lokomat conditions in terms of inter-joint coordination between hip–knee (r = 0.67–0.91), hip–ankle (r = 0.66–0.85), and knee–ankle (r = 0.90–0.95). This study showed that some gait determinants, such as pelvis obliquity, rotation, and hip adduction, are altered when walking with Lokomat in comparison to a Treadmill. Kinematic deviations induced by the Lokomat were most prominent at high levels of bodyweight support. Interestingly, different levels of Guidance did not affect gait kinematics. The present results can help therapists to adequately select settings during Lokomat therapy. Full article

(This article belongs to the Special Issue Design and Application of Wearable and Rehabilitation Robotics)

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21 pages, 3797 KiB

Open AccessArticle

Customized Trajectory Optimization and Compliant Tracking Control for Passive Upper Limb Rehabilitation

by Liaoyuan Li, Jianhai Han, Xiangpan Li, Bingjing Guo and Xinjie Wang

Sensors 2023, 23(15), 6953; https://doi.org/10.3390/s23156953 - 4 Aug 2023

Cited by 1 | Viewed by 739

Abstract

Passive rehabilitation training in the early poststroke period can promote the reshaping of the nervous system. The trajectory should integrate the physicians’ experience and the patient’s characteristics. And the training should have high accuracy on the premise of safety. Therefore, trajectory customization, optimization, and tracking control algorithms are conducted based on a new upper limb rehabilitation robot. First, joint friction and initial load were identified and compensated. The admittance algorithm was used to realize the trajectory customization. Second, the improved butterfly optimization algorithm (BOA) was used to optimize the nonuniform rational B-spline fitting curve (NURBS). Then, a variable gain control strategy is designed, which enables the robot to track the trajectory well with small human–robot interaction (HRI) forces and to comply with a large HRI force to ensure safety. Regarding the return motion, an error subdivision method is designed to slow the return movement. The results showed that the customization force is less than 6 N. The trajectory tracking error is within 12 mm without a large HRI force. The control gain starts to decrease in 0.5 s periods while there is a large HRI force, thereby improving safety. With the decrease in HRI force, the real position can return to the desired trajectory slowly, which makes the patient feel comfortable. Full article

(This article belongs to the Special Issue Design and Application of Wearable and Rehabilitation Robotics)

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