Journal articles: 'Rehabilitation Glove'

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Reddy, Raja Vikram, and Aliasgar Barodawala. "Hand Rehabilitation Glove." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (August 31, 2018): 1392–96. http://dx.doi.org/10.31142/ijtsrd17028.

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Ilyas, Salman Muhammad, Syed Faraz Jawed, Choudhary Sobhan Shakeel, Luqman Hashim Bawany, and Rumaisa Amin. "DESIGN AND DEVELOPMENT OF A STROKE REHABILITATION GLOVE FOR MEASURING AND MONITORING HAND MOTIONS." Pakistan Journal of Rehabilitation 11, no. 2 (July 7, 2022): 167–78. http://dx.doi.org/10.36283/pjr.zu.11.2/023.

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Muscular weakness tends to increase very rapidly due to various medical illnesses such as stroke, paralysis, fibromyalgia, etc. In order to keep tracks of the rehabilitative progress of patients who are suffering from such diseases, it is necessary to acquire data pertaining to finger movements including flexion and extension. Along with range of motions of proximal interphalangeal (PIP), distal interphalangeal (DIP) and meta-capo phalangeal joints, pinching strength is also vital in assessing the progress of rehabilitative therapies. Hence, our objective is to develop an assistive technology in the form of a smart glove comprising of flex and force sensors for measuring flexion and extension movements as well as the pinching strength. To the best of author’s knowledge, commercially available rehabilitation gloves are expensive and have some limitations such as being non-portable, having an antenna mount on the gloves facing upward and so on. The smart glove was able to measure the flexion and extension of finger movements and pinch strength with low-power requirements and low cost associated with production. The flexion and extension of finger movements along with pinching strength of stroke survivors was measured with the aid of the glove and showed promising outcomes. Through the results achieved by our developed glove, we were able to analyze the rehabilitative progress of stroke survivors. Moreover, the data is monitored continuously through liquid crystal display for rehabilitation purposes. Notably, this low cost glove was designed with the aid of flex sensors and force sensors that enabled the effective measurement of flexion, extension and pinching strength of stroke survivors.

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Ahmed, Yahya, Auns Al-Neami, and Saleem Lateef. "Robotic Glove for Rehabilitation Purpose: Review." 3D SCEEER Conference sceeer, no. 3d (July 1, 2020): 86–92. http://dx.doi.org/10.37917/ijeee.sceeer.3rd.12.

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Rehabilitation robots have become one of the main technical instruments that Treat disorder patients in the biomedical engineering field. The robotic glove for the rehabilitation is basically made of specialized materials which can be designed to help the post-stroke patients. In this paper, a review of the different types of robotic glove for Rehabilitation have been discussed and summarized. This study reviews a different mechanical system of robotic gloves in previous years. The selected studies have been classified into four types according to the Mechanical Design: The first type is a tendon-driven robotic glove. The second type of robotic glove works with a soft actuator as a pneumatic which is operated by air pressure that passes through a plastic pipe, pressure valves, and air compressor. The third type is the exoskeleton robotic gloves this type consists of a wearable mechanical design that can used a finger-based sensor to measure grip strength or is used in interactive video applications. And the fourth type is the robotic glove with a liner actuator this type consists of a tape placed on the fingers and connected to linear actuators to open and close the fingers during the rehabilitation process.

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Taylor, Jamie, and Kevin Curran. "Glove-Based Technology in Hand Rehabilitation." International Journal of Innovation in the Digital Economy 6, no. 1 (January 2015): 29–49. http://dx.doi.org/10.4018/ijide.2015010103.

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Injuries to the hand are more common than those of any other body region and can have considerable financial, time-measured and psychological impact on not only the victim but the community as a whole. Hand rehabilitation aims to return people to their pre-injury roles and occupations and has proved largely successful in doing so with the potential for technology to improve these results further. However, most technology used in hand rehabilitation is based on expensive and non-durable glove-based systems and issues with accuracy are common among those which are not glove-based. The authors outline an accurate, affordable and portable solution wherein the authors use the Leap Motion as a tool for hand rehabilitation. User feedback will be given primarily through an animated 3d hand model as the user performs rehabilitative exercises. Exercise results will be recorded for later viewing by patients and clinicians. The system will also include Gamification aspects, techniques which (while proven to increase participation) have seen little to no use in hand-rehabilitation systems.

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Seçkin, Mine, and Necla Yaman Turan. "Rehabilitation Glove Device Design." Journal of Engineering Technology and Applied Sciences 3, no. 1 (May 30, 2018): 75–81. http://dx.doi.org/10.30931/jetas.391297.

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Guo, Kai, Senhao Zhang, Shasha Zhao, and Hongbo Yang. "Design and Manufacture of Data Gloves for Rehabilitation Training and Gesture Recognition Based on Flexible Sensors." Journal of Healthcare Engineering 2021 (December 7, 2021): 1–9. http://dx.doi.org/10.1155/2021/6359403.

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This work takes the production and usage scenarios of the data glove as the research object and studies the method of applying the flexible sensor to the data glove. Many studies are also devoted to exploring the transplantation of flexible sensors to data gloves. However, this type of research still lacks the display of specific application scenarios such as gesture recognition or hand rehabilitation training. A small amount of experimental data and theoretical analysis are difficult to promote the development of flexible sensors and flexible data gloves design schemes. Therefore, this study uses the self-made flexible sensor of the research group as the core sensing unit to produce a flexible data glove to monitor the bending changes of the knuckles and then use it for simple gesture recognition and rehabilitation training.

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Aghil, T., S. Rahul, S. Buvan Kumaar, Yati Vijay, S. Tharun Kumar, and B. Sidhharth. "A Futuristic Approach for Stroke Rehabilitation Using Smart Gloves." Journal of Physics: Conference Series 2115, no. 1 (November 1, 2021): 012025. http://dx.doi.org/10.1088/1742-6596/2115/1/012025.

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Abstract Stroke is a serious, common, and assured as a global health issue across the globe. Stroke is one of most common cause of death and is a leading cause of impairment in adults. Despite all impressive progression and development in the treatment of stroke, without effective modes of care most stroke patients care will continue to rely on physiotherapy involvement. The purpose of this paper is to explain about a new and better device which helps patients affected by stroke who are not able to move their hands. To rehabilitate stroke survivors, the proposed prototype is designed such that it is a portable smart glove which helps users to regain their muscle memory by continuously contracting and releasing their muscles without the involvement of physiotherapist. This device/glove also consists of sensors that collect and send data to UI using ESP32. This UI is available for the doctors to see the statistics of glove usage and monitors the patient’s conditions. The Glove uses a soft robotics approach to replicate the human hand. The Glove initially aims to contract and release all the muscles in the hand in regular intervals of time. This muscle movement aims to build lost muscle memory.

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Zhu, Yinlong, Weizhuang Gong, Kaimei Chu, Xu Wang, Zhiqiang Hu, and Haijun Su. "A Novel Wearable Soft Glove for Hand Rehabilitation and Assistive Grasping." Sensors 22, no. 16 (August 21, 2022): 6294. http://dx.doi.org/10.3390/s22166294.

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In order to assist patients with finger rehabilitation training and grasping objects, we propose a new type of soft rehabilitation gloves (SRGs), which has both flexion/extension and abduction/adduction movement function for every finger. This paper describes the structure design of the bending actuator and rotating actuator, the fabrication process of the soft actuator, and the implementation of the soft wearable gloves based on a fabric glove. FEM simulation analysis and experiments were conducted to characterize the mechanical behavior and performance of the soft glove in terms of the angle output and force output upon pressurization. To operate this soft wearable glove, we designed the hardware system for SRGs with a flexible strain sensor and force sensor in the loop and introduced a force/position hybrid PID control algorithm to regulate the pressure inputted. Experiment evaluation focused on rehabilitation training gestures; motions and the precise grasping assistance function were executed. The rotating actuator between each finger can supply abduction/adduction motion manner for patients, which will improve rehabilitation effect. The experimental results demonstrated that the developed SRGs have the potential to improve hand movement freedom and the range of grasping successfully.

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Rogriguez, Natalia, Matteo Sangalli, Monika Smukowska, and Mario Covarrubias. "Haptic Feedback Glove for Arm Rehabilitation." Computer-Aided Design and Applications 19, no. 6 (March 9, 2022): 1143–53. http://dx.doi.org/10.14733/cadaps.2022.1143-1153.

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Connolly, James, Joan Condell, Kevin Curran, and Philip Gardiner. "Improving Data Glove Accuracy and Usability Using a Neural Network When Measuring Finger Joint Range of Motion." Sensors 22, no. 6 (March 14, 2022): 2228. http://dx.doi.org/10.3390/s22062228.

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Data gloves capable of measuring finger joint kinematics can provide objective range of motion information useful for clinical hand assessment and rehabilitation. Data glove sensors are strategically placed over specific finger joints to detect movement of the wearers’ hand. The construction of the sensors used in a data glove, the number of sensors used, and their positioning on each finger joint are influenced by the intended use case. Although most glove sensors provide reasonably stable linear output, this stability is influenced externally by the physical structure of the data glove sensors, as well as the wearer’s hand size relative to the data glove, and the elastic nature of materials used in its construction. Data gloves typically require a complex calibration method before use. Calibration may not be possible when wearers have disabled hands or limited joint flexibility, and so limits those who can use a data glove within a clinical context. This paper examines and describes a unique approach to calibration and angular calculation using a neural network that improves data glove repeatability and accuracy measurements without the requirement for data glove calibration. Results demonstrate an overall improvement in data glove measurements. This is particularly relevant when the data glove is used with those who have limited joint mobility and cannot physically complete data glove calibration.

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Proulx, Camille E., Myrka Beaulac, Mélissa David, Catryne Deguire, Catherine Haché, Florian Klug, Mario Kupnik, Johanne Higgins, and Dany H. Gagnon. "Review of the effects of soft robotic gloves for activity-based rehabilitation in individuals with reduced hand function and manual dexterity following a neurological event." Journal of Rehabilitation and Assistive Technologies Engineering 7 (January 2020): 205566832091813. http://dx.doi.org/10.1177/2055668320918130.

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Despite limited scientific evidence, there is an increasing interest in soft robotic gloves to optimize hand- and finger-related functional abilities following a neurological event. This review maps evidence on the effects and effectiveness of soft robotic gloves for hand rehabilitation and, whenever possible, patients’ satisfaction. A systematized search of the literature was conducted using keywords structured around three areas: technology attributes, anatomy, and rehabilitation. A total of 272 titles, abstracts, and keywords were initially retrieved, and data were extracted out of 13 articles. Six articles investigated the effects of wearing a soft robotic glove and eight studied the effect or effectiveness of an intervention with it. Some statistically significant and meaningful beneficial effects were confirmed with the 29 outcome measures used. Finally, 11 articles also confirmed users’ satisfaction with regard to the soft robotic glove, while some articles also noticed an increased engagement in the rehabilitation program with this technology. Despite the heterogeneity across studies, soft robotic gloves stand out as a safe and promising technology to improve hand- and finger-related dexterity and functional performance. However, strengthened evidence of the effects or effectiveness of such devices is needed before their transition from laboratory to clinical practice.

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Fernández-Vázquez, Diego, Roberto Cano-de-la-Cuerda, and Víctor Navarro-López. "Haptic Glove Systems in Combination with Semi-Immersive Virtual Reality for Upper Extremity Motor Rehabilitation after Stroke: A Systematic Review and Meta-Analysis." International Journal of Environmental Research and Public Health 19, no. 16 (August 20, 2022): 10378. http://dx.doi.org/10.3390/ijerph191610378.

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Background: The effectiveness of the virtual reality (VR) for the upper extremity (UE) motor rehabilitation after stroke has been widely studied. However, the effectiveness of the combination between rehabilitation gloves and semi-immersive VR (SVR) compared to conventional treatment has not yet been studied. Methods: A systematic search was conducted in Pubmed, Web of Science, PEDRo, and Scopus, Cochrane, CINHAAL databases from inception to May 2022. Randomized controlled trials were included if patients were under rehabilitation with haptic gloves combined with SVR intervention focused on the UE rehabilitation in stroke patients. Risk of bias and methodological quality were evaluated with the Physiotherapy Evidence Database (PEDro), and the modified Cochrane library criteria. A random effects model was used for the quantitative assessment of the included studies using the standard mean difference with a 95% confidence interval. Heterogeneity among the included studies was assessed using Cochran’s Q test and the incoherence index (I2). Results: After a first screening, seven studies were included. Significant differences with a 95% confidence interval were obtained in favor of the rehabilitation glove combined with SVR in the short term (SMD—standardized mean differences = 0.38, 95% CI—confidence interval = 0.20; 0.56; Z: 4.24; p =< 0.001). In the long term, only the studies that performed an intervention based in rehabilitation glove combined with SVR with also included rehabilitation were able to maintain the improvements (SMD = 0.71, 95% CI = 0.40; 1.02; Z: 4.48; p =< 0.001). Conclusions: The combined use of rehabilitation haptic gloves and SVR with conventional rehabilitation produces significant improvements with respect to conventional rehabilitation treatment alone in terms of functionality of the UE in stroke patients.

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Tavares, Rafael, Paulo Abreu, and Manuel Rodrigues Quintas. "Data Acquisition Glove for Hand Movement Impairment Rehabilitation." International Journal of Online Engineering (iJOE) 12, no. 04 (April 28, 2016): 52. http://dx.doi.org/10.3991/ijoe.v12i04.5141.

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The present paper describes a data acquisition wearable device for hand rehabilitation. The main goal of this glove is to be used by patients with hand movement impairment. It has position sensors to measure the bending of synovial joints and sensors to measure the fingertip contact pressure. There is a coin motor and a LED placed on each finger to produce a vibratory and visual stimulus. The glove also tracks the hand rotation and translation using a MPU (Motion Processing Unit) that contains an accelerometer and a gyroscope. A graphical application for an HMI module was developed in order to create rehabilitation game like exercises where sensor data can be logged for further analysis by a therapist. The wearable device electronic hardware comprises a Glove module and an HMI module that communicate through SPI protocol (Serial Peripheral Interface). The wearable device supports USB connection to send data to a computer or to be used as a peripheral device in virtual or augmented reality applications.

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Hadi, Alireza, Khalil Alipour, Saber Kazeminasab, and Mohammad Elahinia. "ASR glove: A wearable glove for hand assistance and rehabilitation using shape memory alloys." Journal of Intelligent Material Systems and Structures 29, no. 8 (December 6, 2017): 1575–85. http://dx.doi.org/10.1177/1045389x17742729.

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Milia, Paolo, MariaCristina Peccini, Federico De Salvo, Alice Sfaldaroli, Chiara Grelli, Giorgia Lucchesi, Nora Sadauskas, Catia Rossi, Marco Caserio, and Mario Bigazzi. "Rehabilitation with robotic glove (Gloreha) in poststroke patients." Digital Medicine 5, no. 2 (2019): 62. http://dx.doi.org/10.4103/digm.digm_3_19.

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Sabry, Sana Sabah, Mouayad Sahib, and Thaker Nayl. "Toward Hand Functions Rehabilitation Using the Virtual World for Pre-school Children with Cerebral Palsy." International Journal of Emerging Technologies in Learning (iJET) 15, no. 09 (May 15, 2020): 110. http://dx.doi.org/10.3991/ijet.v15i09.13047.

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Cerebral Palsy (CP)is a collection of permanent, non-progressive disorders that impact the individual’s motor ability. The rehabilitation of patients with CP is very important to improve their motor abilities and to minimize the need for third parties. In this paper, a low-cost hand rehabilitation glove based on finger bend/pressure analysis is presented. The data glove is used to improve hand functioning for pre-school children with cerebral palsy through virtual reality games. The system consists of two parts: the data glove and several virtual games. The data glove consists of a microcontrol-ler, flex sensors, force sensors and radiofrequency transmission units. The use of the newly developed system will assist psychotherapist to follow the CP child daily, weekly or monthly. The rehabilitation model and the predicted physiotherapy results can be extracted from the patient’s record after using the data. Experimental results have shown that the regular usage for the data glove improved 75 % of the participants’ fingers bending angel and the child’s grip ability.

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Smit, Gerwin, Dick Plettenburg, and Frans Van der Helm. "A mechanism to compensate undesired stiffness in joints of prosthetic hands." Prosthetics and Orthotics International 38, no. 2 (May 20, 2013): 96–102. http://dx.doi.org/10.1177/0309364613488620.

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Background:Cosmetic gloves that cover a prosthetic hand have a parasitic positive stiffness that counteracts the flexion of a finger joint.Objectives:Reducing the required input torque to move a finger of a prosthetic hand by compensating the parasitic stiffness of the cosmetic glove.Study design:Experimental, test bench.Methods:The parasitic positive stiffness and the required input torques of a polyvinyl chloride glove and a silicone glove were measured when flexing a metacarpophalangeal finger joint for 90°. To compensate this positive stiffness, an adjustable compensation mechanism with a negative stiffness was designed and built. A MATLAB model was created to predict the optimal settings of the mechanism, based on the measured stiffness, in order to minimize the required input torque of the total system. The mechanism was tested in its optimal setting with an applied glove.Results:The mechanism reduced the required input torque by 58% for the polyvinyl chloride glove and by 52% for the silicone glove. The total energy dissipation of the joint did not change significantly.Conclusions:This study shows that the undesired positive stiffness in the joint can be compensated with a relatively simple negative stiffness mechanism, which fits inside a finger of a standard cosmetic glove.Clinical relevanceThis study presents a mechanism that compensates the undesired stiffness of cosmetic gloves on prosthetic hands. As a result, it requires less input force, torque and energy to move the fingers. Application of this mechanism in body-powered hands will reduce the control effort of the user.

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Ribas Neto, Antonio, Julio Fajardo, Willian Hideak Arita da Silva, Matheus Kaue Gomes, Maria Claudia Ferrari de Castro, Eric Fujiwara, and Eric Rohmer. "Design of Tendon-Actuated Robotic Glove Integrated with Optical Fiber Force Myography Sensor." Automation 2, no. 3 (September 3, 2021): 187–201. http://dx.doi.org/10.3390/automation2030012.

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People taken by upper limb disorders caused by neurological diseases suffer from grip weakening, which affects their quality of life. Researches on soft wearable robotics and advances in sensor technology emerge as promising alternatives to develop assistive and rehabilitative technologies. However, current systems rely on surface electromyography and complex machine learning classifiers to retrieve the user intentions. In addition, the grasp assistance through electromechanical or fluidic actuators is passive and does not contribute to the rehabilitation of upper-limb muscles. Therefore, this paper presents a robotic glove integrated with a force myography sensor. The glove-like orthosis features tendon-driven actuation through servo motors, working as an assistive device for people with hand disabilities. The detection of user intentions employs an optical fiber force myography sensor, simplifying the operation beyond the usual electromyography approach. Moreover, the proposed system applies functional electrical stimulation to activate the grasp collaboratively with the tendon mechanism, providing motion support and assisting rehabilitation.

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Ma, Zhou, Pinhas Ben-Tzvi, and Jerome Danoff. "Hand Rehabilitation Learning System With an Exoskeleton Robotic Glove." IEEE Transactions on Neural Systems and Rehabilitation Engineering 24, no. 12 (December 2016): 1323–32. http://dx.doi.org/10.1109/tnsre.2015.2501748.

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Lin, M. X., G. Y. Ma, F. Q. Liu, Q. S. Sun, and A. Q. Song. "Design and Dynamic Modeling of Flexible Rehabilitation Mechanical Glove." IOP Conference Series: Materials Science and Engineering 320 (March 2018): 012002. http://dx.doi.org/10.1088/1757-899x/320/1/012002.

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Sareen, Anirudh, Avirbhav Singh, Anupreet Sinha, Abhishek Arya, Atharv Arya, Gaurav Sapra, Rajesh Kumar, Parveen Kumar, and Damanpreet Singh. "Design and fabrication of prosthetic glove for hand rehabilitation." Materials Today: Proceedings 28 (2020): 1612–15. http://dx.doi.org/10.1016/j.matpr.2020.04.849.

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Taguchi, Kan, Takashi Yamada, and Kumiko Yoshida. "2P2-A08 Development of Power Assist Glove for Rehabilitation." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2006 (2006): _2P2—A08_1—_2P2—A08_3. http://dx.doi.org/10.1299/jsmermd.2006._2p2-a08_1.

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KOIZUMI, Shoichiro, Te-Hsin CHANG, Hiroyuki NABAE, Gen ENDO, Koichi SUZUMORI, Motoki MITA, Kimio SAITO, Kazutoshi HATAKEYAMA, Satoaki CHIDA, and Yoichi SHIMADA. "Prototype of Hand Rehabilitation Glove with Thin McKibben Muscles." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2019 (2019): 1P1—A08. http://dx.doi.org/10.1299/jsmermd.2019.1p1-a08.

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Duanmu, Dehao, Xiaojun Wang, Xiaodong Li, Zheng Wang, and Yong Hu. "Design of Guided Bending Bellows Actuators for Soft Hand Function Rehabilitation Gloves." Actuators 11, no. 12 (November 25, 2022): 346. http://dx.doi.org/10.3390/act11120346.

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This study developed a soft pneumatic glove actuated by elliptical cross-sectional guided bending bellows to augment finger-knuckle rehabilitation for patients with hand dysfunction. The guided bending bellows actuators (GBBAs) are made of thermoplastic elastomer (TPE) materials, demonstrating the necessary air tightness as a pneumatic actuator. The GBBAs could produce different moments of inertia when increasing internal air pressure drives the GBBAs bending along distinct symmetry planes and exhibits anisotropic kinematic bending performance. Actuated by GBBAs, wearable soft rehabilitation gloves can be used for daily rehabilitation training of hand dysfunction to enhance the range of motion of the finger joint. To control each finger of the gloves independently to achieve the function of manipulating gestures, a multi-channel pneumatic control system is designed, and each air circuit is equipped with an air-pressure sensor to make adjustments based on feedback. Compared with general soft robotic exoskeleton gloves currently used for hand dysfunction, the GBBAs actuated soft gloves have the advantage of enhancing the rehabilitation strength, finger movement range, and multi-action coordination applied with guided bending bellows actuators.

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Thimabut, Wachirayongyot, Pim Terachinda, and Wasuwat Kitisomprayoonkul. "Effectiveness of a Soft Robotic Glove to Assist Hand Function in Stroke Patients: A Cross-Sectional Pilot Study." Rehabilitation Research and Practice 2022 (April 25, 2022): 1–8. http://dx.doi.org/10.1155/2022/3738219.

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Purpose. Stroke patients have difficulty performing tasks using their paretic hands. There are limited data on the effects of using a soft robotic glove to assist with hand function. The objective of this study was to investigate the effectiveness of a soft robotic glove in assisting hand function in stroke patients. Methods. This study was a cross-sectional pilot study. Twenty stroke patients with partial or complete hand weakness were recruited from a rehabilitation centre. The Box and Block Test (BBT) and the Action Research Arm Test (ARAT) were performed under two conditions: with and without use of the soft robotic glove. The order of the conditions was randomly assigned by a computer-generated program. Results. BBT scores increased 6.4 blocks when using the soft robotic glove ( p < 0.001 ). ARAT grasp, grip, pinch, and overall scores increased by 27.08% ( p < 0.01 ), 28.75% ( p < 0.001 ), 15.89% ( p < 0.01 ), and 21.15% ( p < 0.001 ), respectively, using the glove versus not using the glove. Conclusions. The findings of this study suggest that using a soft robotic glove can assist a poststroke paretic hand in executing grasp, grip, and pinch.

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Thimabut, Wachirayongyot, Pim Terachinda, and Wasuwat Kitisomprayoonkul. "Effectiveness of a Soft Robotic Glove to Assist Hand Function in Stroke Patients: A Cross-Sectional Pilot Study." Rehabilitation Research and Practice 2022 (April 25, 2022): 1–8. http://dx.doi.org/10.1155/2022/3738219.

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Purpose. Stroke patients have difficulty performing tasks using their paretic hands. There are limited data on the effects of using a soft robotic glove to assist with hand function. The objective of this study was to investigate the effectiveness of a soft robotic glove in assisting hand function in stroke patients. Methods. This study was a cross-sectional pilot study. Twenty stroke patients with partial or complete hand weakness were recruited from a rehabilitation centre. The Box and Block Test (BBT) and the Action Research Arm Test (ARAT) were performed under two conditions: with and without use of the soft robotic glove. The order of the conditions was randomly assigned by a computer-generated program. Results. BBT scores increased 6.4 blocks when using the soft robotic glove ( p < 0.001 ). ARAT grasp, grip, pinch, and overall scores increased by 27.08% ( p < 0.01 ), 28.75% ( p < 0.001 ), 15.89% ( p < 0.01 ), and 21.15% ( p < 0.001 ), respectively, using the glove versus not using the glove. Conclusions. The findings of this study suggest that using a soft robotic glove can assist a poststroke paretic hand in executing grasp, grip, and pinch.

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Popović, Dejan, Aleksandar Stojanović, Andjelka Pjanović, Slobodanka Radosavljević, Mirjana Popović, Stevan Jović, and Dragan Vulović. "Clinical evaluation of the bionic glove." Archives of Physical Medicine and Rehabilitation 80, no. 3 (March 1999): 299–304. http://dx.doi.org/10.1016/s0003-9993(99)90141-7.

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Cheng, Nicholas, Kok Soon Phua, Hwa Sen Lai, Pui Kit Tam, Ka Yin Tang, Kai Kei Cheng, Raye Chen-Hua Yeow, Kai Keng Ang, Cuntai Guan, and Jeong Hoon Lim. "Brain-Computer Interface-Based Soft Robotic Glove Rehabilitation for Stroke." IEEE Transactions on Biomedical Engineering 67, no. 12 (December 2020): 3339–51. http://dx.doi.org/10.1109/tbme.2020.2984003.

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Borghetti, Michela, Emilio Sardini, and Mauro Serpelloni. "Sensorized Glove for Measuring Hand Finger Flexion for Rehabilitation Purposes." IEEE Transactions on Instrumentation and Measurement 62, no. 12 (December 2013): 3308–14. http://dx.doi.org/10.1109/tim.2013.2272848.

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Polygerinos, Panagiotis, Zheng Wang, Kevin C. Galloway, Robert J. Wood, and Conor J. Walsh. "Soft robotic glove for combined assistance and at-home rehabilitation." Robotics and Autonomous Systems 73 (November 2015): 135–43. http://dx.doi.org/10.1016/j.robot.2014.08.014.

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NAKAMURA, Issei, and Manabu ONO. "Research of a Rehabilitation Glove driven by Pneumatic Bellows Actuators." Proceedings of Conference of Tohoku Branch 2017.52 (2017): 177. http://dx.doi.org/10.1299/jsmeth.2017.52.177.

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Montanez, Ginna A. Parra, Sarlos Siri Adema, and Hannia Pacheco Gutiérrez. "634 Use and Benefits of Using Acrylic Splints Inside the Pressotherapy Glove in Pediatric Burned Hands, Six Years Retrospective Review." Journal of Burn Care & Research 41, Supplement_1 (March 2020): S161. http://dx.doi.org/10.1093/jbcr/iraa024.255.

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Abstract Introduction Burns children’s hands in special care areas. This report describes 1. how to handle using acrylic splints burned hands in pressotherapy gloves and 2. how to make splints using cuts and remaining parts of the face acrylic splints. The hand splints offer comfort, long-lasting, and full fit and produce full passive stretching of the scar During the maturation period. This splints are easy to be handled by parents. Methods We developed splints were to be placed inside gloves. For this process and given the selected material, it was agreed with the seamstress that the glove as appropriate, We have developed an alternative method of using the material. The splints are constructed as follows: 1. It′s purchase in sheets, although the cuts or parts that are face masks with acrylic reused.2.To copy the mold wax pencil is required. 3. To mold hot air gun is required. 4. It is easy to wash and sand to the finish. Results A pilot trial of 40 splints was undertaken and once it was deemed safe to use it became our practice. Over the last 6 years, 486 acrylic splints have been fabricated and utilized in over 210 surgical cases to address splint needs for the hand. This technique with acrylic is used as a material available for making face masks which minimizes costs. The use of splints inside the glove allows full adjustment over the scar counteracting the forces of retraction of these scars and also at a palmar and digital level. With the use of splints parents said there is a best period of adaptation of children and they do not remove them. Conclusions Acrylic with silicone is lightweight and flexible, being comfortable for children to use inside the glove. It is used especially in evening hours, but can also be used in the day, in any case accompany the use of the splint with a strengthening program, tactile desensitization and manual stretching to prevent muscle atrophy and deconditioning. For molding the acrylic, gloves and hot air gun are needed to cut more easily and thus give the required angulation required in each case. Care should be taken strictly using acrylic splints to prevent compressive neuropathies. It should be checked rigorously usage time of the splints during the first week review possible areas of pressure on edges, inside the glove, to prevent secondary complications. Applicability of Research to Practice

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Wa’ie Hazman, Muhammad Ajwad, Ili Najaa Aimi Mohd Nordin, Faridah Hanim Mohd Noh, Nurulaqilla Khamis, M. R. M. Razif, Ahmad Athif Faudzi, and Asyikin Sasha Mohd Hanif. "IMU sensor-based data glove for finger joint measurement." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (October 1, 2020): 82. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp82-88.

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<p>The methods used to quantify finger range of motion significantly influence how hand disability is reported. To date, the accuracy of sensors being utilized in data gloves from the literature has been ascertained yet need further analysis. This paper presents an inertial measurement unit sensor-based data glove for finger joint measurement developed for collecting a range of motion data of distal interphalangeal, proximal interphalangeal and metacarpophalangeal finger joints of an index finger. In this study, three inertial measurement sensors, MPU-6050 and two flexible bend sensors which are capable to detect angle displacement were attached to the distal interphalangeal, proximal interphalangeal and metacarpophalangeal finger joint points on the glove. The data taken from inertial measurement unit sensors and flexible bend sensors were acquired using Arduino and MATLAB software interface. The data obtained were compared with the reference data measured from goniometer to allow for accurate comparative measurement. The percentage of error resulted from MPU-6050 sensor unit were ranged from 0.81 % to 5.41 % were very low which indicates high accuracy when compared with the measurements obtained using goniometer. On the other hand, flexible bend sensor shows low accuracy (11.11 % to 19.35 % error). In conclusion, the inertial measurement unit sensor-based data glove using MPU-6050 sensors can be a reliable solution for tracking the progress of finger rehabilitation exercises. In order to motivate patients to adhere to the therapy exercises, interactive rehabilitation game will be developed in the future incorporating MPU-6050 sensors on all five fingers.</p>

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Rand, D. T., and A. C. Nicol. "An Instrumented Glove for Monitoring MCP Joint Motion." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 207, no. 4 (December 1993): 207–10. http://dx.doi.org/10.1243/pime_proc_1993_207_298_02.

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A lycra glove has been instrumented with novel low-profile electrogoniometers to measure index and middle finger metacarpophalangeal (MCP) flexion/extension motion. It is lightweight and comfortable to wear, enabling portable, unobtrusive measurement of joint usage about the home or work environment. Preliminary results have shown it to be of comparable accuracy to existing clinical measurements. Many applications are envisaged in the fields of ergonomics, orthopaedics and rehabilitation.

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Yang, Shih-Hung, Chia-Lin Koh, Chun-Hang Hsu, Po-Chuan Chen, Jia-Wei Chen, Yu-Hao Lan, Yi Yang, et al. "An Instrumented Glove-Controlled Portable Hand-Exoskeleton for Bilateral Hand Rehabilitation." Biosensors 11, no. 12 (December 3, 2021): 495. http://dx.doi.org/10.3390/bios11120495.

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Effective bilateral hand training is desired in rehabilitation programs to restore hand function for people with unilateral hemiplegia, so that they can perform daily activities independently. However, owing to limited human resources, the hand function training available in current clinical settings is significantly less than the adequate amount needed to drive optimal neural reorganization. In this study, we designed a lightweight and portable hand exoskeleton with a hand-sensing glove for bilateral hand training and home-based rehabilitation. The hand-sensing glove measures the hand movement of the less-affected hand using a flex sensor. Thereafter, the affected hand is driven by the hand exoskeleton using the measured hand movements. Compared with the existing hand exoskeletons, our hand exoskeleton improves the flexible mechanism for the back of the hand for better wearing experience and the thumb mechanism to make the pinch gesture possible. We designed a virtual reality game to increase the willingness of repeated movement practice for rehabilitation. Our system not only facilitates bilateral hand training but also assists in activities of daily living. This system could be beneficial for patients with hemiplegia for starting correct and sufficient hand function training in the early stages to optimize their recovery.

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Han, Xiaoxue, Xuhong Miao, Xi Chen, Gaoming Jiang, and Li Niu. "Research on finger movement sensing performance of conductive gloves." Journal of Engineered Fibers and Fabrics 14 (January 2019): 155892501988762. http://dx.doi.org/10.1177/1558925019887622.

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Knitted flexible sensors are sensors based on the loop structure of knitted fabric, which are soft and close-fitting. Monitoring finger motion can obtain useful information for some applications such as rehabilitation medicine, sports bionics, or human–computer interaction. In this paper, a conductive glove was knitted by SHIMA Seiki SWG 061N-15G computerized flat knitting machine. One experimenter wore it to measure motions data of index finger. The glove has a conductive intarsia area knitted by silver-nylon filaments. The experimenter performed static and dynamic test of hand posture, respectively, then observed the effect of figure bending characteristics on the glove resistance data. The result showed that human finger motion can be monitored successfully by the conductive glove without hard transducers, and both of the bending rate ( Br) and bending angle of the finger proximal interphalangeal joint ( Pba) affect the resistance change of the conductive area of the glove. In other words, the conductive glove has potentials to monitor and reflect human finger motions in detail.

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B, Aparna, Anithakrithi B, Naveena P, Yaswanth Kumar M, Avinash M, and S. Sivanandam. "Design and simulation of bionic glove for rehabilitation of the paralytics." International Journal of Engineering & Technology 7, no. 2.8 (March 19, 2018): 1. http://dx.doi.org/10.14419/ijet.v7i2.8.10314.

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Repetitive therapy can improve dexterity and hand movement among the paralyzed and stroke affected patients. The assistance of simple robotic technology may enhance the recovery rate of such patients. The study aims at developing a low cost bionic glove rehabilitation device which aids in providing effective finger exercises for physiotherapy by the use of a potentiometer. The prototype is designed in the form of a wearable glove for easy use. It includes an ATMEGA-328 microcontroller that is programmed using Arduino software for controlling the device. The motion of the fingers during therapy is achieved using a metal gear servo motor while the linear potentiometer controls the angle. This device can be used in rehabilitation to provide repetitive therapy for fingers at home with limited supervision by the physiotherapist for the paralytics. The performance of the device is simulated and evaluated using the Proteus Intelligent Schematic Input System (ISIS) software. The results obtained from the simulation can be used to improve the features of the device for effective practical implementation.

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Wan, Khairunizam, A. R. Aswad, Rashidah Suhaimi, Nazrul H. Adnan, D. Hazry, Zuradzman M. Razlan, A. B. Shahriman, Mohd Asri Ariffin, and M. Haslina. "Glove-Based Virtual Interaction for the Rehabilitation of Hemiparesis Stroke Patient." Journal of Robotics, Networking and Artificial Life 1, no. 2 (2014): 130. http://dx.doi.org/10.2991/jrnal.2014.1.2.7.

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Placidi, Giuseppe, Luigi Cinque, Matteo Polsinelli, and Matteo Spezialetti. "Measurements by A LEAP-Based Virtual Glove for the Hand Rehabilitation." Sensors 18, no. 3 (March 10, 2018): 834. http://dx.doi.org/10.3390/s18030834.

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USHIZAWA, Miki, and Manabu ONO. "OS1211 Fabrication of a Rehabilitation Glove driven by Pneumatic Bellows Actuators." Proceedings of Conference of Kanto Branch 2016.22 (2016): _OS1211–1_—_OS1211–2_. http://dx.doi.org/10.1299/jsmekanto.2016.22._os1211-1_.

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Janarthanan, Vinesh, Md Assad-Uz-Zaman, Mohammad Habibur Rahman, Erin McGonigle, and Inga Wang. "Design and development of a sensored glove for home-based rehabilitation." Journal of Hand Therapy 33, no. 2 (April 2020): 209–19. http://dx.doi.org/10.1016/j.jht.2020.03.023.

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Zhao, Shumi, Jianxun Liu, Zidan Gong, Yisong Lei, Xia OuYang, Chi Chiu Chan, and Shuangchen Ruan. "Wearable Physiological Monitoring System Based on Electrocardiography and Electromyography for Upper Limb Rehabilitation Training." Sensors 20, no. 17 (August 28, 2020): 4861. http://dx.doi.org/10.3390/s20174861.

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Secondary injuries are common during upper limb rehabilitation training because of uncontrollable physical force and overexciting activities, and long-time training may cause fatigue and reduce the training effect. This study proposes a wearable monitoring device for upper limb rehabilitation by integrating electrocardiogram and electromyogram (ECG/EMG) sensors and using data acquisition boards to obtain accurate signals during robotic glove assisting training. The collected ECG/EMG signals were filtered, amplified, digitized, and then transmitted to a remote receiver (smart phone or laptop) via a low-energy Bluetooth module. A software platform was developed for data analysis to visualize ECG/EMG information, and integrated into the robotic glove control module. In the training progress, various hand activities (i.e., hand closing, forearm pronation, finger flexion, and wrist extension) were monitored by the EMG sensor, and the changes in the physiological status of people (from excited to fatigue) were monitored by the ECG sensor. The functionality and feasibility of the developed physiological monitoring system was demonstrated by the assisting robotic glove with an adaptive strategy for upper limb rehabilitation training improvement. The feasible results provided a novel technique to monitor individual ECG and EMG information holistically and practically, and a technical reference to improve upper limb rehabilitation according to specific treatment conditions and the users’ demands. On the basis of this wearable monitoring system prototype for upper limb rehabilitation, many ECG-/EMG-based mobile healthcare applications could be built avoiding some complicated implementation issues such as sensors management and feature extraction.

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Edwick, Dale O., Dana A. Hince, Jeremy M. Rawlins, Fiona M. Wood, and Dale W. Edgar. "Randomized Controlled Trial of Compression Interventions for Managing Hand Burn Edema, as Measured by Bioimpedance Spectroscopy." Journal of Burn Care & Research 41, no. 5 (June 29, 2020): 992–99. http://dx.doi.org/10.1093/jbcr/iraa104.

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Abstract Compression, a common treatment of choice for the management of edema, is one intervention that is applied with little objective understanding of the optimal parameters of application or efficacy in acute burn wounds. The aim of this study was to determine the effectiveness of different methods of compression for the management of hand edema following burn injury. The primary hypothesis tested was that in acute hand burn injury, the application of cohesive bandage will reduce edema faster than a generic compression glove. It is a randomized controlled study of 100 patients presenting with hand burn injury. Compression was randomized to one of the three methods of application: 1) spiral application of Coban to fingers, figure of eight to hand and wrist; 2) pinch application of Coban to fingers, spiral application to hand and wrist; or 3) a generic compression glove (control condition). Bioimpedance spectroscopy was used to measure hand volumes. Hand and wrist range of movement, pain scores, and QuickDASH were recorded. One hundred patients (68 males) demonstrated significant reductions in hand volumes, using all compression methods. Both methods of applying Coban resulted in significantly greater reductions in edema compared to the generic compression glove. Notwithstanding compression method, all range of movement measures improved, with significant improvement in thumb opposition (P = .046), hand span (P = .020), and wrist flexion (P = .020). QuickDASH decreased between sessions (P < .001). Different methods of applying Coban are superior to generic compression gloves for managing acute hand burn edema.

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Li, Fengguan, Jiahong Chen, Guanpeng Ye, Siwei Dong, Zishu Gao, and Yitong Zhou. "Soft Robotic Glove with Sensing and Force Feedback for Rehabilitation in Virtual Reality." Biomimetics 8, no. 1 (February 15, 2023): 83. http://dx.doi.org/10.3390/biomimetics8010083.

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Many diseases, such as stroke, arthritis, and spinal cord injury, can cause severe hand impairment. Treatment options for these patients are limited by expensive hand rehabilitation devices and dull treatment procedures. In this study, we present an inexpensive soft robotic glove for hand rehabilitation in virtual reality (VR). Fifteen inertial measurement units are placed on the glove for finger motion tracking, and a motor—tendon actuation system is mounted onto the arm and exerts forces on fingertips via finger-anchoring points, providing force feedback to fingers so that the users can feel the force of a virtual object. A static threshold correction and complementary filter are used to calculate the finger attitude angles, hence computing the postures of five fingers simultaneously. Both static and dynamic tests are performed to validate the accuracy of the finger-motion-tracking algorithm. A field-oriented-control-based angular closed-loop torque control algorithm is adopted to control the force applied to the fingers. It is found that each motor can provide a maximum force of 3.14 N within the tested current limit. Finally, we present an application of the haptic glove in a Unity-based VR interface to provide the operator with haptic feedback while squeezing a soft virtual ball.

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Young, Douglas E., Doris Trachtman, Irving S. Scher, and Richard A. Schmidt. "High School and College Baseball Pitchers' Response and Glove Movements to Line Drives." Journal of Applied Biomechanics 22, no. 1 (February 2006): 25–32. http://dx.doi.org/10.1123/jab.22.1.25.

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The timing of glove movements used by baseball pitchers to catch fast approaching balls (i.e., line drives) was examined in two tests to determine the responses and temporal characteristics of glove movements in high school and college baseball pitchers. Balls were projected toward the head of participants at 34.8 m·s–1 (78 mph) on average in an indoor test and at speeds approaching 58.1 m·s–1 (130 mph) in a field test. Pitchers caught over 80% and 15% of the projected balls in the indoor and field tests, respectively. Analyses of glove responses indicated that all pitchers could track the line drives and produce coordinated glove movements, which were initiated 160 ms (± 47.8), on average, after the ball was launched. College pitchers made initial glove movements sooner than high school pitchers in the field test (p = 0.012). In contrast, average glove velocity for pitchers increased from 1.33 (± 0.61) to 3.45 (± 0.86) m·s–1 across the tests, but did not differ between experience levels. Glove movement initiation and speed were unrelated, and pitchers utilized visual information throughout the ball's flight to catch balls that approached at speeds exceeding the estimated speeds in competitive situations.

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Rieger, Claire, and Jaydip Desai. "A Preliminary Study to Design and Evaluate Pneumatically Controlled Soft Robotic Actuators for a Repetitive Hand Rehabilitation Task." Biomimetics 7, no. 4 (September 20, 2022): 139. http://dx.doi.org/10.3390/biomimetics7040139.

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A stroke is an infarction in the cortical region of the brain that often leads to isolated hand paresis. This common side effect renders individuals compromised in their ability to actively flex or extend the fingers of the affected hand. While there are currently published soft robotic glove designs, this article proposed a unique design that allows users to self-actuate their therapy due to the ability to re-extend the hand using a layer of resistive flexible steel. The results showed a consistently achieved average peak of 75° or greater for each finger while the subjects’ hands were at rest during multiple trials of pneumatic assisted flexion. During passive assisted testing, human subject testing on 10 participants showed that these participants were able to accomplish 80.75% of their normal active finger flexion range with the steel-layer-lined pneumatic glove and 87.07% with the unlined pneumatic glove on average when neglecting outliers. An addition of the steel layer lowered the blocked tip force by an average of 18.13% for all five fingers. These data show strong evidence that this glove would be appropriate to advance to human subject testing on those who do have post stroke hand impairments.

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Sarwat, Hussein, Hassan Sarwat, Shady A. Maged, Tamer H. Emara, Ahmed M. Elbokl, and Mohammed Ibrahim Awad. "Design of a Data Glove for Assessment of Hand Performance Using Supervised Machine Learning." Sensors 21, no. 21 (October 20, 2021): 6948. http://dx.doi.org/10.3390/s21216948.

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The large number of poststroke recovery patients poses a burden on rehabilitation centers, hospitals, and physiotherapists. The advent of rehabilitation robotics and automated assessment systems can ease this burden by assisting in the rehabilitation of patients with a high level of recovery. This assistance will enable medical professionals to either better provide for patients with severe injuries or treat more patients. It also translates into financial assistance as well in the long run. This paper demonstrated an automated assessment system for in-home rehabilitation utilizing a data glove, a mobile application, and machine learning algorithms. The system can be used by poststroke patients with a high level of recovery to assess their performance. Furthermore, this assessment can be sent to a medical professional for supervision. Additionally, a comparison between two machine learning classifiers was performed on their assessment of physical exercises. The proposed system has an accuracy of 85% (±5.1%) with careful feature and classifier selection.

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Feng, Yongfei, Mingwei Zhong, Xusheng Wang, Hao Lu, Hongbo Wang, Pengcheng Liu, and Luige Vladareanu. "Active triggering control of pneumatic rehabilitation gloves based on surface electromyography sensors." PeerJ Computer Science 7 (April 19, 2021): e448. http://dx.doi.org/10.7717/peerj-cs.448.

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The portable and inexpensive hand rehabilitation robot has become a practical rehabilitation device for patients with hand dysfunction. A pneumatic rehabilitation glove with an active trigger control system is proposed, which is based on surface electromyography (sEMG) signals. It can trigger the hand movement based on the patient’s hand movement trend, which may improve the enthusiasm and efficiency of patient training. Firstly, analysis of sEMG sensor installation position on human’s arm and signal acquisition process were carried out. Then, according to the statistical law, three optimal eigenvalues of sEMG signals were selected as the follow-up neural network classification input. Using the back propagation (BP) neural network, the classifier of hand movement is established. Moreover, the mapping relationship between hand sEMG signals and hand actions is built by training and testing. Different patients choose the same optimal eigenvalues, and the calculation formula of eigenvalues’ amplitude is unique. Due to the differences among individuals, the weights and thresholds of each node in the BP neural network model corresponding to different patients are not the same. Therefore, the BP neural network model library is established, and the corresponding network is called for operation when different patients are trained. Finally, based on sEMG signal trigger, the pneumatic glove training control algorithm was proposed. The combination of the trigger signal waveform and the motion signal waveform indicates that the pneumatic rehabilitation glove is triggered to drive the patient’s hand movement. Preliminary tests have confirmed that the accuracy rate of trend recognition for hand movement is about 90%. In the future, clinical trials of patients will be conducted to prove the effectiveness of this system.

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Mylon, Peter, Roger Lewis, Matt J. Carré, and Nicolas Martin. "A critical review of glove and hand research with regard to medical glove design." Ergonomics 57, no. 1 (November 12, 2013): 116–29. http://dx.doi.org/10.1080/00140139.2013.853104.

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Jumphoo, Talit, Monthippa Uthansakul, Pumin Duangmanee, Naeem Khan, and Peerapong Uthansakul. "Soft Robotic Glove Controlling Using Brainwave Detection for Continuous Rehabilitation at Home." Computers, Materials & Continua 66, no. 1 (2020): 961–76. http://dx.doi.org/10.32604/cmc.2020.012433.

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Journal articles on the topic 'Rehabilitation Glove'

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