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Ismail R, Ariyanto M, Setiawan JD, Hidayat T, Paryanto, Nuswantara LK. Design and testing of fabric-based portable soft exoskeleton glove for hand grasping assistance in daily activity. HARDWAREX 2024; 18:e00537. [PMID: 38784668 PMCID: PMC11111837 DOI: 10.1016/j.ohx.2024.e00537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 04/27/2024] [Accepted: 05/02/2024] [Indexed: 05/25/2024]
Abstract
Hand exoskeleton robots have been developed as rehabilitation robots and assistive devices. Based on the material used, they can be soft or hard exoskeletons. Soft materials such as fabric can be used as a component of the wearable robot to increase comfortability. In this paper, we proposed an affordable soft hand exoskeleton based on fabric and motor-tendon actuation for hand flexion/extension motion assistance in daily activities. On-off control and PI compensator were implemented to regulate finger flexion and extension of the soft exoskeleton. The controllers were embedded into a microcontroller using Simulink software. The input signal command comes from the potentiometer and electromyography (EMG) sensor to drive the flexion/extension movement. Based on the experiments, the proposed controller successfully controlled the exoskeleton hand to facilitate a user in grasping various objects. The proposed soft hand exoskeleton is lightweight, comfortable, portable, and affordable, making it easily manufactured using available hardware and open-source code. The developed soft exoskeleton is a potential assistive device for a person who lost the ability to grasp objects.
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Affiliation(s)
- Rifky Ismail
- Depertment of Mechanical Engineering, Faculty of Engineering, Diponegoro University, Semarang, Indonesia
- Center for Biomechanics Biomaterials Biomechatronics and Biosignal Processing (CBIOM3S) Diponegoro University, Semarang, Indonesia
- Professional Education of Engineers, Faculty of Engineering, Diponegoro University, Semarang, Indonesia
| | - Mochammad Ariyanto
- Depertment of Mechanical Engineering, Faculty of Engineering, Diponegoro University, Semarang, Indonesia
- Graduate School of Engineering, Mechanical Engineering Department, Osaka University, Suita, Japan
| | - Joga D. Setiawan
- Depertment of Mechanical Engineering, Faculty of Engineering, Diponegoro University, Semarang, Indonesia
- Center for Biomechanics Biomaterials Biomechatronics and Biosignal Processing (CBIOM3S) Diponegoro University, Semarang, Indonesia
| | - Taufik Hidayat
- Depertment of Mechanical Engineering, Faculty of Engineering, Diponegoro University, Semarang, Indonesia
- Center for Biomechanics Biomaterials Biomechatronics and Biosignal Processing (CBIOM3S) Diponegoro University, Semarang, Indonesia
| | - Paryanto
- Depertment of Mechanical Engineering, Faculty of Engineering, Diponegoro University, Semarang, Indonesia
- Professional Education of Engineers, Faculty of Engineering, Diponegoro University, Semarang, Indonesia
| | - Limbang K. Nuswantara
- Professional Education of Engineers, Faculty of Engineering, Diponegoro University, Semarang, Indonesia
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2
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Chan HL, Meng LF, Kao YA, Chang YJ, Chang HW, Chen SW, Wu CY. Myoelectric, Myo-Oxygenation, and Myotonometry Changes during Robot-Assisted Bilateral Arm Exercises with Varying Resistances. SENSORS (BASEL, SWITZERLAND) 2024; 24:1061. [PMID: 38400219 PMCID: PMC10892273 DOI: 10.3390/s24041061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 01/26/2024] [Accepted: 02/02/2024] [Indexed: 02/25/2024]
Abstract
Robot-assisted bilateral arm training has demonstrated its effectiveness in improving motor function in individuals post-stroke, showing significant enhancements with increased repetitions. However, prolonged training sessions may lead to both mental and muscle fatigue. We conducted two types of robot-assisted bimanual wrist exercises on 16 healthy adults, separated by one week: long-duration, low-resistance workouts and short-duration, high-resistance exercises. Various measures, including surface electromyograms, near-infrared spectroscopy, heart rate, and the Borg Rating of Perceived Exertion scale, were employed to assess fatigue levels and the impacts of exercise intensity. High-resistance exercise resulted in a more pronounced decline in electromyogram median frequency and recruited a greater amount of hemoglobin, indicating increased muscle fatigue and a higher metabolic demand to cope with the intensified workload. Additionally, high-resistance exercise led to increased sympathetic activation and a greater sense of exertion. Conversely, engaging in low-resistance exercises proved beneficial for reducing post-exercise muscle stiffness and enhancing muscle elasticity. Choosing a low-resistance setting for robot-assisted wrist movements offers advantages by alleviating mental and physiological loads. The reduced training intensity can be further optimized by enabling extended exercise periods while maintaining an approximate dosage compared to high-resistance exercises.
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Affiliation(s)
- Hsiao-Lung Chan
- Department of Electrical Engineering, Chang Gung University, Taoyuan 33302, Taiwan; (H.-L.C.); (Y.-A.K.); (H.-W.C.)
- Neuroscience Research Center, Chang Gung Memorial Hospital, Linkou, Taoyuan 33305, Taiwan;
| | - Ling-Fu Meng
- Department of Occupational Therapy and Graduate Institute of Behavioral Sciences, Chang Gung University, Taoyuan 33302, Taiwan;
- Division of Occupational Therapy, Department of Rehabilitation, Chiayi Chang Gung Memorial Hospital, Chiayi 61363, Taiwan
| | - Yung-An Kao
- Department of Electrical Engineering, Chang Gung University, Taoyuan 33302, Taiwan; (H.-L.C.); (Y.-A.K.); (H.-W.C.)
| | - Ya-Ju Chang
- Neuroscience Research Center, Chang Gung Memorial Hospital, Linkou, Taoyuan 33305, Taiwan;
- School of Physical Therapy and Graduate Institute of Rehabilitation Science, Chang Gung University, Taoyuan 33302, Taiwan
| | - Hao-Wei Chang
- Department of Electrical Engineering, Chang Gung University, Taoyuan 33302, Taiwan; (H.-L.C.); (Y.-A.K.); (H.-W.C.)
| | - Szi-Wen Chen
- Neuroscience Research Center, Chang Gung Memorial Hospital, Linkou, Taoyuan 33305, Taiwan;
- Department of Electronic Engineering, Chang Gung University, Taoyuan 33302, Taiwan
| | - Ching-Yi Wu
- Department of Occupational Therapy and Graduate Institute of Behavioral Sciences, Chang Gung University, Taoyuan 33302, Taiwan;
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, Linkou, Taoyuan 33305, Taiwan
- Healthy Aging Research Center, Chang Gung University, Taoyuan 33302, Taiwan
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3
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Ren H, Liu T, Wang J. Design and Analysis of an Upper Limb Rehabilitation Robot Based on Multimodal Control. SENSORS (BASEL, SWITZERLAND) 2023; 23:8801. [PMID: 37960505 PMCID: PMC10647264 DOI: 10.3390/s23218801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/24/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023]
Abstract
To address the rehabilitation needs of upper limb hemiplegic patients in various stages of recovery, streamline the workload of rehabilitation professionals, and provide data visualization, our research team designed a six-degree-of-freedom upper limb exoskeleton rehabilitation robot inspired by the human upper limb's structure. We also developed an eight-channel synchronized signal acquisition system for capturing surface electromyography (sEMG) signals and elbow joint angle data. Utilizing Solidworks, we modeled the robot with a focus on modularity, and conducted structural and kinematic analyses. To predict the elbow joint angles, we employed a back propagation neural network (BPNN). We introduced three training modes: a PID control, bilateral control, and active control, each tailored to different phases of the rehabilitation process. Our experimental results demonstrated a strong linear regression relationship between the predicted reference values and the actual elbow joint angles, with an R-squared value of 94.41% and an average error of four degrees. Furthermore, these results validated the increased stability of our model and addressed issues related to the size and single-mode limitations of upper limb rehabilitation robots. This work lays the theoretical foundation for future model enhancements and further research in the field of rehabilitation.
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Affiliation(s)
- Hang Ren
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200000, China;
| | - Tongyou Liu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 201100, China;
| | - Jinwu Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200000, China;
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 201100, China;
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4
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Guo K, Orban M, Lu J, Al-Quraishi MS, Yang H, Elsamanty M. Empowering Hand Rehabilitation with AI-Powered Gesture Recognition: A Study of an sEMG-Based System. Bioengineering (Basel) 2023; 10:bioengineering10050557. [PMID: 37237627 DOI: 10.3390/bioengineering10050557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 04/30/2023] [Accepted: 05/03/2023] [Indexed: 05/28/2023] Open
Abstract
Stroke is one of the most prevalent health issues that people face today, causing long-term complications such as paresis, hemiparesis, and aphasia. These conditions significantly impact a patient's physical abilities and cause financial and social hardships. In order to address these challenges, this paper presents a groundbreaking solution-a wearable rehabilitation glove. This motorized glove is designed to provide comfortable and effective rehabilitation for patients with paresis. Its unique soft materials and compact size make it easy to use in clinical settings and at home. The glove can train each finger individually and all fingers together, using assistive force generated by advanced linear integrated actuators controlled by sEMG signals. The glove is also durable and long-lasting, with 4-5 h of battery life. The wearable motorized glove is worn on the affected hand to provide assistive force during rehabilitation training. The key to this glove's effectiveness is its ability to perform the classified hand gestures acquired from the non-affected hand by integrating four sEMG sensors and a deep learning algorithm (the 1D-CNN algorithm and the InceptionTime algorithm). The InceptionTime algorithm classified ten hand gestures' sEMG signals with an accuracy of 91.60% and 90.09% in the training and verification sets, respectively. The overall accuracy was 90.89%. It showed potential as a tool for developing effective hand gesture recognition systems. The classified hand gestures can be used as a control command for the motorized wearable glove placed on the affected hand, allowing it to mimic the movements of the non-affected hand. This innovative technology performs rehabilitation exercises based on the theory of mirror therapy and task-oriented therapy. Overall, this wearable rehabilitation glove represents a significant step forward in stroke rehabilitation, offering a practical and effective solution to help patients recover from stroke's physical, financial, and social impact.
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Affiliation(s)
- Kai Guo
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Mostafa Orban
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- Mechanical Department, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt
| | - Jingxin Lu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130001, China
| | | | - Hongbo Yang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130001, China
| | - Mahmoud Elsamanty
- Mechanical Department, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt
- Mechatronics and Robotics Department, School of Innovative Design Engineering, Egypt-Japan University of Science and Technology, Alexandria 21934, Egypt
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Liu C, Li J, Zhang S, Yang H, Guo K. Study on Flexible sEMG Acquisition System and Its Application in Muscle Strength Evaluation and Hand Rehabilitation. MICROMACHINES 2022; 13:mi13122047. [PMID: 36557346 PMCID: PMC9782516 DOI: 10.3390/mi13122047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/18/2022] [Accepted: 11/20/2022] [Indexed: 06/01/2023]
Abstract
Wearable devices based on surface electromyography (sEMG) to detect muscle activity can be used to assess muscle strength with the development of hand rehabilitation applications. However, conventional acquisition devices are usually complicated to operate and poorly comfortable for more medical and scientific application scenarios. Here, we report a flexible sEMG acquisition system that combines a graphene-based flexible electrode with a signal acquisition flexible printed circuit (FPC) board. Our system utilizes a polydimethylsiloxane (PDMS) substrate combined with graphene transfer technology to develop a flexible sEMG sensor. The single-lead sEMG acquisition system was designed and the FPC board was fabricated considering the requirements of flexible bending and twisting. We demonstrate the above design approach and extend this flexible sEMG acquisition system to applications for assessing muscle strength and hand rehabilitation training using a long- and short-term memory network training model trained to predict muscle strength, with 98.81% accuracy in the test set. The device exhibited good flexion and comfort characteristics. In general, the ability to accurately and imperceptibly monitor surface electromyography (EMG) signals is critical for medical professionals and patients.
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Affiliation(s)
- Chang Liu
- College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Jiuqiang Li
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Senhao Zhang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Hongbo Yang
- College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Kai Guo
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
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Jain S, Doriya R. Security framework to healthcare robots for secure sharing of healthcare data from cloud. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY 2022; 14:2429-2439. [PMID: 35633945 PMCID: PMC9128770 DOI: 10.1007/s41870-022-00997-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 05/06/2022] [Indexed: 11/04/2022]
Abstract
Healthcare robots have the potential to assist medical practitioners in accurately performing tasks such as nursing, diagnosing, and performing critical surgeries. Limited processing, battery power, and storage capacity may reduce the robot's working efficiency. Using cloud services (massive storage, fast processing, and network) overcome the above-mentioned issues. However, sharing healthcare data from the cloud to healthcare robots raises security concerns. Sharing sensitive healthcare data, from the cloud to healthcare robots, can lead to multiple internal and external attacks that are an important research issue. To avoid these types of attacks, data must be encrypted before it is stored in the cloud, assigned roles using access controls, and maintains the integrity of the data. In this paper, the robotics healthcare data is encrypted using an Elliptic Curve Cryptography (ECC)-based mechanism for secure sharing, and Hash-based Message Authentication Code-SHA 1 (HMAC-SHA1) is used for maintaining the integrity of the sensitive data. The results show that the proposed methodology gives better results with less security overhead. Furthermore, the proposed framework can be applied practically in a healthcare environment with low computational power.
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Tosin MC, Bagesteiro LB, Balbinot A. Actor-Critic Reinforcement Learning Based Algorithm for Contaminant Minimization in sEMG Movement Recognition. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3636-3639. [PMID: 36086267 DOI: 10.1109/embc48229.2022.9871412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
This paper aims to present an approach based on Reinforcement Learning (RL) concept to detect contaminants' type and minimize their effect on surface electromyography signal (sEMG) based movement recognition. The referred method was applied in the pre-processing stage of a sEMG based motion classification system using the Ninapro database 2 artificially contaminated with electrocardiography (ECG) interference, motion artifact (MOA), powerline interference (PLI) and additive white Gaussian noise (WGN). Support Vector Machine was the method for movement classification. The results showed an improvement of 8.9%, 16.7%, 15.9%, 16.5%, and 11.9% in the movement recognition accuracy with the application of the pre-processing algorithm to restore, respectively, one, three, six, nine, and 12 contaminated channels.
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8
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Flexible Control Strategy for Upper-Limb Rehabilitation Exoskeleton Based on Virtual Spring Damper Hypothesis. ACTUATORS 2022. [DOI: 10.3390/act11050138] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The focus of this work is to design a control strategy with the dynamic characteristics of spring damping to realize the virtual flexibility and softness of a rigid-joint exoskeleton without installing real, physical elastic devices. The basic idea of a “virtual softening control strategy” for a single rigid joint is that a virtual spring damper (VSD) is installed between the motor and the output shaft. By designing the control signal of the motor, the torque output of the joint actuator is softened so that the output has the characteristics of elasticity and variable stiffness. The transfer velocity profile of human limbs reaching from one posture to another always presents as bell-shaped. According to this characteristic, we constructed a trajectory planning method for a point-to-point position-tracking controller based on a normal distribution function, and it was successfully applied to the control of 5-DoF upper-limb rehabilitation exoskeleton. A multi-joint cooperative flexible controller based on the virtual spring damper hypothesis (VSDH) was successfully applied to solve the constrained control problem of the exoskeletons and the self-motion problem caused by redundant degrees of freedom (DoFs). The stability of the closed-loop controlled system is theoretically proven by use of the scalar energy function gradient method and the Riemann metric convergence analysis method.
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De la Cruz-Sánchez BA, Arias-Montiel M, Lugo-González E. EMG-controlled hand exoskeleton for assisted bilateral rehabilitation. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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10
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Butz B, Jussen A, Rafi A, Lux G, Gerken J. A Taxonomy for Augmented and Mixed Reality Applications to Support Physical Exercises in Medical Rehabilitation—A Literature Review. Healthcare (Basel) 2022; 10:healthcare10040646. [PMID: 35455824 PMCID: PMC9028587 DOI: 10.3390/healthcare10040646] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 03/16/2022] [Accepted: 03/26/2022] [Indexed: 12/13/2022] Open
Abstract
In the past 20 years, a vast amount of research has shown that Augmented and Mixed Reality applications can support physical exercises in medical rehabilitation. In this paper, we contribute a taxonomy, providing an overview of the current state of research in this area. It is based on a comprehensive literature review conducted on the five databases Web of Science, ScienceDirect, PubMed, IEEE Xplore, and ACM up to July 2021. Out of 776 identified references, a final selection was made of 91 papers discussing the usage of visual stimuli delivered by AR/MR or similar technology to enhance the performance of physical exercises in medical rehabilitation. The taxonomy bridges the gap between a medical perspective (Patient Type, Medical Purpose) and the Interaction Design, focusing on Output Technologies and Visual Guidance. Most approaches aim to improve autonomy in the absence of a therapist and increase motivation to improve adherence. Technology is still focused on screen-based approaches, while the deeper analysis of Visual Guidance revealed 13 distinct, reoccurring abstract types of elements. Based on the analysis, implications and research opportunities are presented to guide future work.
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Affiliation(s)
- Benjamin Butz
- Institute for Innovation Research and Management, Westphalian University of Applied Sciences, 44801 Bochum, Germany
- Correspondence:
| | - Alexander Jussen
- Human-Computer Interaction Group, Westphalian University of Applied Sciences, 45897 Gelsenkirchen, Germany; (A.J.); (J.G.)
| | - Asma Rafi
- Computer Graphics Group, Westphalian University of Applied Sciences, 45897 Gelsenkirchen, Germany; (A.R.); (G.L.)
| | - Gregor Lux
- Computer Graphics Group, Westphalian University of Applied Sciences, 45897 Gelsenkirchen, Germany; (A.R.); (G.L.)
| | - Jens Gerken
- Human-Computer Interaction Group, Westphalian University of Applied Sciences, 45897 Gelsenkirchen, Germany; (A.J.); (J.G.)
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Secciani N, Brogi C, Pagliai M, Buonamici F, Gerli F, Vannetti F, Bianchini M, Volpe Y, Ridolfi A. Wearable Robots: An Original Mechatronic Design of a Hand Exoskeleton for Assistive and Rehabilitative Purposes. Front Neurorobot 2021; 15:750385. [PMID: 34744679 PMCID: PMC8568131 DOI: 10.3389/fnbot.2021.750385] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 09/15/2021] [Indexed: 11/13/2022] Open
Abstract
Robotic devices are being employed in more and more sectors to enhance, streamline, and augment the outcomes of a wide variety of human activities. Wearable robots arise indeed as of-vital-importance tools for telerehabilitation or home assistance targeting people affected by motor disabilities. In particular, the field of “Robotics for Medicine and Healthcare” is attracting growing interest. The development of such devices is a primarily addressed topic since the increasing number of people in need of rehabilitation or assistive therapies (due to population aging) growingly weighs on the healthcare systems of the nation. Besides, the necessity to move to clinics represents an additional logistic burden for patients and their families. Among the various body parts, the hand is specially investigated since it most ensures the independence of an individual, and thus, the restoration of its dexterity is considered a high priority. In this study, the authors present the development of a fully wearable, portable, and tailor-made hand exoskeleton designed for both home assistance and telerehabilitation. Its purpose is either to assist patients during activities of daily living by running a real-time intention detection algorithm or to be used for remotely supervised or unsupervised rehabilitation sessions by performing exercises preset by therapists. Throughout the mechatronic design process, special attention has been paid to the complete wearability and comfort of the system to produce a user-friendly device capable of assisting people in their daily life or enabling recorded home rehabilitation sessions allowing the therapist to monitor the state evolution of the patient. Such a hand exoskeleton system has been designed, manufactured, and preliminarily tested on a subject affected by spinal muscular atrophy, and some results are reported at the end of the article.
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Affiliation(s)
- Nicola Secciani
- Department of Industrial Engineering, University of Florence, Firenze, Italy
| | - Chiara Brogi
- Department of Industrial Engineering, University of Florence, Firenze, Italy
| | - Marco Pagliai
- Department of Industrial Engineering, University of Florence, Firenze, Italy
| | - Francesco Buonamici
- Department of Industrial Engineering, University of Florence, Firenze, Italy
| | - Filippo Gerli
- IRCCS Don Gnocchi, Don Carlo Gnocchi Foundation, Firenze, Italy
| | | | - Massimo Bianchini
- Institute for Complex Systems, National Research Council, Sesto Fiorentino, Italy
| | - Yary Volpe
- Department of Industrial Engineering, University of Florence, Firenze, Italy
| | - Alessandro Ridolfi
- Department of Industrial Engineering, University of Florence, Firenze, Italy
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12
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Design and Development of an Upper Limb Rehabilitative Robot with Dual Functionality. MICROMACHINES 2021; 12:mi12080870. [PMID: 34442492 PMCID: PMC8400039 DOI: 10.3390/mi12080870] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/09/2021] [Accepted: 07/19/2021] [Indexed: 11/23/2022]
Abstract
The design of an upper limb rehabilitation robot for post-stroke patients is considered a benchmark problem regarding improving functionality and ensuring better human–robot interaction (HRI). Existing upper limb robots perform either joint-based exercises (exoskeleton-type functionality) or end-point exercises (end-effector-type functionality). Patients may need both kinds of exercises, depending on the type, level, and degree of impairments. This work focused on designing and developing a seven-degrees-of-freedom (DoFs) upper-limb rehabilitation exoskeleton called ‘u-Rob’ that functions as both exoskeleton and end-effector types device. Furthermore, HRI can be improved by monitoring the interaction forces between the robot and the wearer. Existing upper limb robots lack the ability to monitor interaction forces during passive rehabilitation exercises; measuring upper arm forces is also absent in the existing devices. This research work aimed to develop an innovative sensorized upper arm cuff to measure the wearer’s interaction forces in the upper arm. A PID control technique was implemented for both joint-based and end-point exercises. The experimental results validated both types of functionality of the developed robot.
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13
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Liu Y, Li X, Zhu A, Zheng Z, Zhu H. Design and evaluation of a surface electromyography-controlled lightweight upper arm exoskeleton rehabilitation robot. INT J ADV ROBOT SYST 2021. [DOI: 10.1177/17298814211003461] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Nowadays, the rehabilitation robot has been developed for rehabilitation therapy. However, there are few studies on upper arm exoskeletons for rehabilitation training of muscle strength. This article aims to design a surface electromyography-controlled lightweight exoskeleton rehabilitation robot for home-based progressive resistance training. The exoskeleton’s lightweight structure is designed based on the kinematic model of the elbow joint and ergonomics sizes of the arm. At the same time, the overall weight of the exoskeleton is controlled at only 3.03 kg. According to the rehabilitation training task, we use torque limit mode to ensure stable torque output at variable velocity. We also propose a surface electromyography-based control method, which uses k- Nearest Neighbor algorithm to classify surface electromyographic signals under progressive training loads, and utilizes principal component analysis to improve the recognition accuracy to control the exoskeleton to provide muscle strength compensation. The assessment experiment of the exoskeleton rehabilitation robot shows that the dynamic recognition accuracy of this control method is 80.21%. Muscle activity of biceps brachii and triceps brachii under each training load decreases significantly when subjects with the exoskeleton robot. The results indicate that the exoskeleton rehabilitation robot can output the corresponding torque to assist in progressive resistance training. This study provides a solution to potential problems in the family-oriented application of exoskeleton rehabilitation robots.
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Affiliation(s)
- Yang Liu
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Xiaoling Li
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Aibin Zhu
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Ziming Zheng
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Huijin Zhu
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
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14
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Bouteraa Y, Abdallah IB, Ibrahim A, Ahanger TA. Fuzzy logic-based connected robot for home rehabilitation. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-201671] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
In this paper, a robotic system dedicated to remote wrist rehabilitation is proposed as an Internet of Things (IoT) application. The system offers patients home rehabilitation. Since the physiotherapist and the patient are on different sites, the system guarantees that the physiotherapist controls and supervises the rehabilitation process and that the patient repeats the same gestures made by the physiotherapist. A human-machine interface (HMI) has been developed to allow the physiotherapist to remotely control the robot and supervise the rehabilitation process. Based on a computer vision system, physiotherapist gestures are sent to the robot in the form of control instructions. Wrist range of motion (RoM), EMG signal, sensor current measurement, and streaming from the patient’s environment are returned to the control station. The various acquired data are displayed in the HMI and recorded in its database, which allows later monitoring of the patient’s progress. During the rehabilitation process, the developed system makes it possible to follow the muscle contraction thanks to an extraction of the Electromyography (EMG) signal as well as the patient’s resistance thanks to a feedback from a current sensor. Feature extraction algorithms are implemented to transform the EMG raw signal into a relevant data reflecting the muscle contraction. The solution incorporates a cascade fuzzy-based decision system to indicate the patient’s pain. As measurement safety, when the pain exceeds a certain threshold, the robot should stop the action even if the desired angle is not yet reached. Information on the patient, the evolution of his state of health and the activities followed, are all recorded, which makes it possible to provide an electronic health record. Experiments on 3 different subjects showed the effectiveness of the developed robotic solution.
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Affiliation(s)
- Yassine Bouteraa
- Digital Research Center of Sfax & CEM Lab-ENIS, University of Sfax, Sfax, Tunisia
- Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | | | - Atef Ibrahim
- Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
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Islam MRU, Waris A, Kamavuako EN, Bai S. A comparative study of motion detection with FMG and sEMG methods for assistive applications. J Rehabil Assist Technol Eng 2020; 7:2055668320938588. [PMID: 33240523 PMCID: PMC7672763 DOI: 10.1177/2055668320938588] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 06/02/2020] [Indexed: 11/16/2022] Open
Abstract
Introduction While surface-electromyography (sEMG) has been widely used in limb motion detection for the control of exoskeleton, there is an increasing interest to use forcemyography (FMG) method to detect motion. In this paper, we review the applications of two types of motion detection methods. Their performances were experimentally compared in day-to-day classification of forearm motions. The objective is to select a detection method suitable for motion assistance on a daily basis. Methods Comparisons of motion detection with FMG and sEMG were carried out considering classification accuracy (CA), repeatability and training scheme. For both methods, classification of motions was achieved through feed-forward neural network. Repeatability was evaluated on the basis of change in CA between days and also training schemes. Results The experiments shows that day-to-day CA with FMG can reach 84.9%, compared with a CA of 77.8% with sEMG, when the classifiers were trained only on the first day. Moreover, the CA with FMG can reach to 86.5%, comparable to CA of 84.1% with sEMG, if classifiers were trained daily. Conclusions Results suggest that data recorded from FMG is more repeatable in day-to-day testing and therefore FMG-based methods can be more useful than sEMG-based methods for motion detection in applications where exoskeletons are used as needed on a daily basis.
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Affiliation(s)
| | - Asim Waris
- Department of Biomedical Engineering and Sciences, National University of Sciences and Technology, Islamabad, Pakistan
| | | | - Shaoping Bai
- Department of Materials and Production, Aalborg University, Aalborg, Denmark
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16
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Onay F, Mert A. Phasor represented EMG feature extraction against varying contraction level of prosthetic control. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101881] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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17
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Li C, Yan Y, Ren H. Compliant Finger Exoskeleton with Telescoping Super-elastic Transmissions. J INTELL ROBOT SYST 2020. [DOI: 10.1007/s10846-020-01186-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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18
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Wu YT, Gomes MK, da Silva WH, Lazari PM, Fujiwara E. Integrated Optical Fiber Force Myography Sensor as Pervasive Predictor of Hand Postures. Biomed Eng Comput Biol 2020; 11:1179597220912825. [PMID: 32269474 PMCID: PMC7093689 DOI: 10.1177/1179597220912825] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 02/18/2020] [Indexed: 11/16/2022] Open
Abstract
Force myography (FMG) is an appealing alternative to traditional electromyography in biomedical applications, mainly due to its simpler signal pattern and immunity to electrical interference. Most FMG sensors, however, send data to a computer for further processing, which reduces the user mobility and, thus, the chances for practical application. In this sense, this work proposes to remodel a typical optical fiber FMG sensor with smaller portable components. Moreover, all data acquisition and processing routines were migrated to a Raspberry Pi 3 Model B microprocessor, ensuring the comfort of use and portability. The sensor was successfully demonstrated for 2 input channels and 9 postures classification with an average precision and accuracy of ~99.5% and ~99.8%, respectively, using a feedforward artificial neural network of 2 hidden layers and a competitive output layer.
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Affiliation(s)
- Yu Tzu Wu
- Laboratory Photonic Materials and Devices, School of Mechanical Engineering, University of Campinas, Campinas, Brazil
| | - Matheus K Gomes
- Laboratory Photonic Materials and Devices, School of Mechanical Engineering, University of Campinas, Campinas, Brazil
| | - Willian Ha da Silva
- Laboratory Photonic Materials and Devices, School of Mechanical Engineering, University of Campinas, Campinas, Brazil
| | - Pedro M Lazari
- Laboratory Photonic Materials and Devices, School of Mechanical Engineering, University of Campinas, Campinas, Brazil
| | - Eric Fujiwara
- Laboratory Photonic Materials and Devices, School of Mechanical Engineering, University of Campinas, Campinas, Brazil
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Mubin O, Alnajjar F, Jishtu N, Alsinglawi B, Al Mahmud A. Exoskeletons With Virtual Reality, Augmented Reality, and Gamification for Stroke Patients' Rehabilitation: Systematic Review. JMIR Rehabil Assist Technol 2019; 6:e12010. [PMID: 31586360 PMCID: PMC6779025 DOI: 10.2196/12010] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 12/09/2018] [Accepted: 07/18/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Robot-assisted therapy has become a promising technology in the field of rehabilitation for poststroke patients with motor disorders. Motivation during the rehabilitation process is a top priority for most stroke survivors. With current advancements in technology there has been the introduction of virtual reality (VR), augmented reality (AR), customizable games, or a combination thereof, that aid robotic therapy in retaining, or increasing the interests of, patients so they keep performing their exercises. However, there are gaps in the evidence regarding the transition from clinical rehabilitation to home-based therapy which calls for an updated synthesis of the literature that showcases this trend. The present review proposes a categorization of these studies according to technologies used, and details research in both upper limb and lower limb applications. OBJECTIVE The goal of this work was to review the practices and technologies implemented in the rehabilitation of poststroke patients. It aims to assess the effectiveness of exoskeleton robotics in conjunction with any of the three technologies (VR, AR, or gamification) in improving activity and participation in poststroke survivors. METHODS A systematic search of the literature on exoskeleton robotics applied with any of the three technologies of interest (VR, AR, or gamification) was performed in the following databases: MEDLINE, EMBASE, Science Direct & The Cochrane Library. Exoskeleton-based studies that did not include any VR, AR or gamification elements were excluded, but publications from the years 2010 to 2017 were included. Results in the form of improvements in the patients' condition were also recorded and taken into consideration in determining the effectiveness of any of the therapies on the patients. RESULTS Thirty studies were identified based on the inclusion criteria, and this included randomized controlled trials as well as exploratory research pieces. There were a total of about 385 participants across the various studies. The use of technologies such as VR-, AR-, or gamification-based exoskeletons could fill the transition from the clinic to a home-based setting. Our analysis showed that there were general improvements in the motor function of patients using the novel interfacing techniques with exoskeletons. This categorization of studies helps with understanding the scope of rehabilitation therapies that can be successfully arranged for home-based rehabilitation. CONCLUSIONS Future studies are necessary to explore various types of customizable games required to retain or increase the motivation of patients going through the individual therapies.
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Affiliation(s)
- Omar Mubin
- School of Computing, Engineering and Mathematics, Western Sydney University, Rydalmere, Australia
| | - Fady Alnajjar
- College of Information Technology, United Arab Emirates University, Al Ain, Abu Dhabi, United Arab Emirates
| | - Nalini Jishtu
- School of Computing, Engineering and Mathematics, Western Sydney University, Rydalmere, Australia
| | - Belal Alsinglawi
- School of Computing, Engineering and Mathematics, Western Sydney University, Rydalmere, Australia
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