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3 pages, 170 KiB

Open AccessEditorial

Special Issue on Robotic-Based Technologies for Rehabilitation and Assistance

by Giuk Lee

Appl. Sci. 2022, 12(16), 8022; https://doi.org/10.3390/app12168022 - 10 Aug 2022

Viewed by 1375

Abstract

Robotic technology designed to assist rehabilitation can potentially increase the efficiency of and accessibility to therapy by assisting therapists in providing consistent training for extended periods of time and collecting data to assess progress [...] Full article

(This article belongs to the Special Issue Robotic-Based Technologies for Rehabilitation and Assistance)

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15 pages, 34754 KiB

Open AccessArticle

Incorporation of Torsion Springs in a Knee Exoskeleton for Stance Phase Correction of Crouch Gait

by Katy Baker Bumbard, Harold Herrington, Chung-Hyun Goh and Alwathiqbellah Ibrahim

Appl. Sci. 2022, 12(14), 7034; https://doi.org/10.3390/app12147034 - 12 Jul 2022

Cited by 4 | Viewed by 2426

Abstract

Crouch gait is a motor complication that is commonly associated with cerebral palsy, spastic diplegia, stroke, and motor-neurological pathologies, broadly defined as knee flexion in excess of 20° in the gait cycle. Uncorrected crouch gait results in fatigue, joint degradation, and loss of [...] Read more.

Crouch gait is a motor complication that is commonly associated with cerebral palsy, spastic diplegia, stroke, and motor-neurological pathologies, broadly defined as knee flexion in excess of 20° in the gait cycle. Uncorrected crouch gait results in fatigue, joint degradation, and loss of ambulation. Torsion springs have been used in cycling to store energy in the knee flexion to reduce fatigue in the quadriceps during knee extension. SolidWorks was used to design a passive exoskeleton for the knee, incorporating torsion springs of stiffnesses 20,000 N/mm and 30,000 N/mm at the knee joint, to correct four different crouch gaits. OpenSim was used to gather data from the moments produced, and knee angles from each crouch gait and the normal gait. Motion analysis of the exoskeleton was simulated using knee angles for each crouch gait and compared with the moments produced with the normal gait moments in the stance phase of the gait cycle. All crouch gait moments were significantly reduced, and the correction of peak crouch moments was achieved, corresponding to the normal gait cycle during the stance phase. These results offer significant potential for nonsurgical and less invasive options for wearable exoskeletons in crouch gait correction. Full article

(This article belongs to the Special Issue Robotic-Based Technologies for Rehabilitation and Assistance)

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24 pages, 6041 KiB

Open AccessArticle

Computer Vision-Based Adaptive Semi-Autonomous Control of an Upper Limb Exoskeleton for Individuals with Tetraplegia

by Stefan Hein Bengtson, Mikkel Berg Thøgersen, Mostafa Mohammadi, Frederik Victor Kobbelgaard, Muhammad Ahsan Gull, Lotte N. S. Andreasen Struijk, Thomas Bak and Thomas B. Moeslund

Appl. Sci. 2022, 12(9), 4374; https://doi.org/10.3390/app12094374 - 26 Apr 2022

Cited by 6 | Viewed by 2671

Abstract

We propose the use of computer vision for adaptive semi-autonomous control of an upper limb exoskeleton for assisting users with severe tetraplegia to increase independence and quality of life. A tongue-based interface was used together with the semi-autonomous control such that individuals with [...] Read more.

We propose the use of computer vision for adaptive semi-autonomous control of an upper limb exoskeleton for assisting users with severe tetraplegia to increase independence and quality of life. A tongue-based interface was used together with the semi-autonomous control such that individuals with complete tetraplegia were able to use it despite being paralyzed from the neck down. The semi-autonomous control uses computer vision to detect nearby objects and estimate how to grasp them to assist the user in controlling the exoskeleton. Three control schemes were tested: non-autonomous (i.e., manual control using the tongue) control, semi-autonomous control with a fixed level of autonomy, and a semi-autonomous control with a confidence-based adaptive level of autonomy. Studies on experimental participants with and without tetraplegia were carried out. The control schemes were evaluated both in terms of their performance, such as the time and number of commands needed to complete a given task, as well as ratings from the users. The studies showed a clear and significant improvement in both performance and user ratings when using either of the semi-autonomous control schemes. The adaptive semi-autonomous control outperformed the fixed version in some scenarios, namely, in the more complex tasks and with users with more training in using the system. Full article

(This article belongs to the Special Issue Robotic-Based Technologies for Rehabilitation and Assistance)

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15 pages, 2219 KiB

Open AccessArticle

Making Best Use of Home-Based Rehabilitation Robots

by Justin F. Gallagher, Manoj Sivan and Martin Levesley

Appl. Sci. 2022, 12(4), 1996; https://doi.org/10.3390/app12041996 - 14 Feb 2022

Cited by 7 | Viewed by 2542

Abstract

Large-scale clinical trials have shown that rehabilitation robots are as affective as conventional therapy, but the cost-effectiveness is preventing their uptake. This study investigated whether a low-cost rehabilitation robot could be deployed in a home setting for rehabilitation of people recovering from stroke [...] Read more.

Large-scale clinical trials have shown that rehabilitation robots are as affective as conventional therapy, but the cost-effectiveness is preventing their uptake. This study investigated whether a low-cost rehabilitation robot could be deployed in a home setting for rehabilitation of people recovering from stroke (n = 16) and whether clinical outcome measures correlated well with kinematic measures gathered by the robot. The results support the feasibility of patients independently using the robot with improvement in both clinical measures and kinematic data. We recommend using kinematic data early in an intervention to detect improvement while using a robotic device. The kinematic measures in the assessment task (hits/minute and normalised jerk) adequately pick up changes within a four-week period, thus allowing the rehabilitation regime to be adapted to suit the user’s needs. Estimating the long-term clinical benefit must be explored in future research. Full article

(This article belongs to the Special Issue Robotic-Based Technologies for Rehabilitation and Assistance)

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18 pages, 2256 KiB

Open AccessArticle

Robotic Device for Out-of-Clinic Post-Stroke Hand Rehabilitation

by Ana Mandeljc, Aleksander Rajhard, Marko Munih and Roman Kamnik

Appl. Sci. 2022, 12(3), 1092; https://doi.org/10.3390/app12031092 - 21 Jan 2022

Cited by 10 | Viewed by 7017

Abstract

Due to the ageing population and an increasing number of stroke patients, we see the potential future of rehabilitation in telerehabilitation, which might alleviate the workload of physiotherapists and occupational therapists. In order to enable the use of telerehabilitation, devices aimed for home [...] Read more.

Due to the ageing population and an increasing number of stroke patients, we see the potential future of rehabilitation in telerehabilitation, which might alleviate the workload of physiotherapists and occupational therapists. In order to enable the use of telerehabilitation, devices aimed for home and independent use need to be developed. This paper describes the design of a robotic device for post-stroke wrist and finger rehabilitation and evaluates the movement it can perform. Six healthy subjects were tested in three experimental conditions: performing a coupled movement of wrist and fingers from flexion to extension without the device, with a passive device, and with an active device. The kinematics of the hand were captured using three Optotrak Certus motion capture systems and tracking 11 infrared active light-emitting diode (LED) markers. The results are presented in the form of base-line trajectories for all middle finger (MF) joints. In addition, the deviations of trajectories between conditions across all subjects were computed for the metacarpophalangeal (MCP) joint and fingertip of the MF and pinkie (PF) finger. Deviations from the base-line trajectory between measurement protocols and the root-mean-square deviation (RMSD) values indicate that the motion of the hand, imposed by the developed device, is comparable to the unconstrained motion of the healthy subjects, especially when moving into the extension, opening the hand. Full article

(This article belongs to the Special Issue Robotic-Based Technologies for Rehabilitation and Assistance)

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18 pages, 40329 KiB

Open AccessArticle

A Self-Adaptive-Coefficient-Double-Power Sliding Mode Control Method for Lower Limb Rehabilitation Exoskeleton Robot

by Yuepeng Zhang, Guangzhong Cao, Wenzhou Li, Jiangcheng Chen, Linglong Li and Dongfeng Diao

Appl. Sci. 2021, 11(21), 10329; https://doi.org/10.3390/app112110329 - 3 Nov 2021

Cited by 14 | Viewed by 2583

Abstract

Lower limb rehabilitation exoskeleton robots have the characteristics of nonlinearity and strong coupling, and they are easily disturbed during operation by environmental factors. Thus, an accurate dynamic model of the robot is difficult to obtain, and achieving trajectory tracking control of the robot [...] Read more.

Lower limb rehabilitation exoskeleton robots have the characteristics of nonlinearity and strong coupling, and they are easily disturbed during operation by environmental factors. Thus, an accurate dynamic model of the robot is difficult to obtain, and achieving trajectory tracking control of the robot is also difficult. In this article, a self-adaptive-coefficient double-power sliding mode control method is proposed to overcome the difficulty of tracking the robot trajectory. The method combines an estimated dynamic model with sliding mode control. A nonlinear control law was designed based on the robot dynamics model and computational torque method, and a compensation term of control law based on double-power reaching law was introduced to reduce the disturbance from model error and environmental factors. The self-adaptive coefficient of the compensation term of the control law was designed to adaptively adjust the compensation term to improve the anti-interference ability of the robot. The simulation and experiment results show that the proposed method effectively improves the trajectory tracking accuracy and anti-interference ability of the robot. Compared with the traditional computed torque method, the proposed method decreases the tracking error by more than 71.77%. The maximum absolute error of the hip joint and knee joint remained below 0.55° and 1.65°, respectively, in the wearable experiment of the robot. Full article

(This article belongs to the Special Issue Robotic-Based Technologies for Rehabilitation and Assistance)

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13 pages, 5625 KiB

Open AccessArticle

Flexible Covariance Matrix Decomposition Method for Data Augmentation and Its Application to Brainwave Signals

by Hoirim Lee, Wonseok Yang and Woochul Nam

Appl. Sci. 2021, 11(20), 9388; https://doi.org/10.3390/app11209388 - 9 Oct 2021

Cited by 2 | Viewed by 2089

Abstract

The acquisition of a large-volume brainwave database is challenging because of the stressful experiments that are required; however, data synthesis techniques can be used to address this limitation. Covariance matrix decomposition (CMD), a widely used data synthesis approach, generates artificial data using the [...] Read more.

The acquisition of a large-volume brainwave database is challenging because of the stressful experiments that are required; however, data synthesis techniques can be used to address this limitation. Covariance matrix decomposition (CMD), a widely used data synthesis approach, generates artificial data using the correlation between features and random noise. However, previous CMD methods constrain the stochastic characteristics of artificial datasets because the random noise used follows a standard distribution. Therefore, this study has improved the performance of CMD by releasing such constraints. Specifically, a generalized normal distribution (GND) was used as it can alter the kurtosis and skewness of the random noise, affecting the distribution of the artificial data. For the validation of GND performance, a motor imagery brainwave classification was conducted on the artificial dataset generated by GND. The GND-based data synthesis increased the classification accuracy obtained with the original data by approximately 8%. Full article

(This article belongs to the Special Issue Robotic-Based Technologies for Rehabilitation and Assistance)

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14 pages, 2845 KiB

Open AccessFeature PaperArticle

Feature Optimization for Gait Phase Estimation with a Genetic Algorithm and Bayesian Optimization

by Wonseok Choi, Wonseok Yang, Jaeyoung Na, Giuk Lee and Woochul Nam

Appl. Sci. 2021, 11(19), 8940; https://doi.org/10.3390/app11198940 - 25 Sep 2021

Cited by 5 | Viewed by 2341

Abstract

For gait phase estimation, time-series data of lower-limb motion can be segmented according to time windows. Time-domain features can then be calculated from the signal enclosed in a time window. A set of time-domain features is used for gait phase estimation. In this [...] Read more.

For gait phase estimation, time-series data of lower-limb motion can be segmented according to time windows. Time-domain features can then be calculated from the signal enclosed in a time window. A set of time-domain features is used for gait phase estimation. In this approach, the components of the feature set and the length of the time window are influential parameters for gait phase estimation. However, optimal parameter values, which determine a feature set and its values, can vary across subjects. Previously, these parameters were determined empirically, which led to a degraded estimation performance. To address this problem, this paper proposes a new feature extraction approach. Specifically, the components of the feature set are selected using a binary genetic algorithm, and the length of the time window is determined through Bayesian optimization. In this approach, the two optimization techniques are integrated to conduct a dual optimization task. The proposed method is validated using data from five walking and five running motions. For walking, the proposed approach reduced the gait phase estimation error from 1.284% to 0.910%, while for running, the error decreased from 1.997% to 1.484%. Full article

(This article belongs to the Special Issue Robotic-Based Technologies for Rehabilitation and Assistance)

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