Posts Tagged hand rehabilitation

[Abstract + References] A New Rehabilitation Device for Finger Extension Movement – Conference paper

Abstract

This paper focuses on the development of a device for hand rehabilitation that can perform the finger extension movement. Developing new rehabilitation systems and devices is necessary since the world population is ageing and spastic hand patients are increasing.

This paper presents a new rehabilitation device that will overcome the limits of most existing rehabilitation mechanisms. Simulations of a conceived system conducted by a CAD code (PTC Creo) showed that the new adopted concepts can be applicable for the finger extension movement.

The results are promising and the potential of the mechanism is believed to be significant. Nevertheless, experimentation on a real prototype will be necessary to fully validate the simulation promising results.

References

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  2. 2.Borboni, A., Mor, M., Faglia, R.: Gloreha–hand robotic rehabilitation: design, mechanical model, and experiments. ASME J. Dyn. Syst. Meas. Control 138(11), 111003 (2016).  https://doi.org/10.1115/1.4033831CrossRefGoogle Scholar
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  9. 9.Aggogeri, F., Mikolajczyk, T., O’Kane, J.: Robotics for rehabilitation of hand movement in stroke survivors. Adv. Mech. Eng. 11, 168781401984192 (2019).  https://doi.org/10.1177/1687814019841921CrossRefGoogle Scholar
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Source: https://link.springer.com/chapter/10.1007/978-3-030-55807-9_72

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[Abstract + Referrences] Interactive and Assistive Gloves for Post-stroke Hand Rehabilitation – Conference paper

Abstract

The inability to fold fingers and move the wrist due to stroke, cardiovascular injuries or emotional shock is one of the most common illnesses wherein conventional rehabilitation therapies are propitious in functional recovery. However, implementation of these methods is laborious, costly and resource-intensive. The structure of the prevailing healthcare system challenges us to design innovative rehabilitation techniques. A desktop-based interactive hand rehabilitation system is, therefore, developed to ensure a more feasible and cost- effective approach. It will encourage a higher number of participation as it is designed to be interesting and interactive than the traditional physiotherapy sessions. The system uses sensor data from Arduino microcontroller and is programmed in Processing IDE allowing user interaction with a virtual environment. The data is further received in an Android application from where it is stored using ThingSpeak Cloud.

References

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  2. 2.Fischer, H. C., Triandafilou, K. M., Thielbar, K. O., Ochoa, J. M., Lazzaro, E. D. C., Pacholski, K. A., et al. (2015). Use of a portable assistive glove to facilitate rehabilitation in stroke survivors with severe hand impairment. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(3), 344–351.CrossRefGoogle Scholar
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  6. 6.Borghetti, M., Sardini, E., & Serpelloni, M. (2013). Sensorized glove for measuring hand finger flexion for rehabilitation purposes. IEEE Transactions on Instrumentation and Measurement, 62(12), 3308–3314.CrossRefGoogle Scholar
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Source: Interactive and Assistive Gloves for Post-stroke Hand Rehabilitation | SpringerLink

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[Abstract] A Wearable Hand Rehabilitation System with Soft Gloves

Abstract

Hand paralysis is one of the most common complications in stroke patients, which severely impacts their daily lives. This paper presents a wearable hand rehabilitation system that supports both mirror therapy and task-oriented therapy. A pair of gloves, i.e., a sensory glove and a motor glove, was designed and fabricated with a soft, flexible material, providing greater comfort and safety than conventional rigid rehabilitation devices. The sensory glove worn on the non-affected hand, which contains the force and flex sensors, is used to measure the gripping force and bending angle of each finger joint for motion detection. The motor glove, driven by micromotors, provides the affected hand with assisted driving-force to perform training tasks. Machine learning is employed to recognize the gestures from the sensory glove and to facilitate the rehabilitation tasks for the affected hand. The proposed system offers 16 kinds of finger gestures with an accuracy of 93.32%, allowing patients to conduct mirror therapy using fine-grained gestures for training a single finger and multiple fingers in coordination. A more sophisticated task-oriented rehabilitation with mirror therapy is also presented, which offers six types of training tasks with an average accuracy of 89.4% in real-time.

via A Wearable Hand Rehabilitation System with Soft Gloves – IEEE Journals & Magazine

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[Abstract] An Ergonomic Solution for Hand Rehabilitation Product Design for Stroke Patients – Conference paper

Abstract

Rehabilitation training is a crucial part that helps stroke patients to train their muscles and rebuild the connection between muscle, nervous system and brain. This study conducts an ergonomic redesign of hand rehabilitation products based on the interview investigation to the user behavior and psychology during the training rehabilitation. The driving part of the device was relocated from the palm to the back of the hand to enhance the experience of stroke subjects to grab objects. Moreover, the device can drive the user to have a positive attitude towards their rehabilitation by visualizing the training improvement. Body scan and 3D printing technologies were utilized to ensure the accuracy of the position of the electric stimulation pads on the hands. The design also enhanced the voluntary motor functions at the palm and the fingers. The research results will contribute in formulating the design criteria of hand rehabilitation training products for stroke subjects.

References

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    Nef, T., Guidalic, M., Riener, R.: ARMin III-arm therapy exoskeleton with an ergonomic shoulder actuation. Appl. Bion. Biomech. 6(2), 127–142 (2009).  https://doi.org/10.1155/2009/962956CrossRefGoogle Scholar
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[Abstract] An Automated Game-Based Variable-Stiffness Exoskeleton for Hand Rehabilitation – Full Text PDF

Abstract

In this paper, we propose and demonstrate the functionality of a novel exoskeleton which provides variable resistance training for human hands. It is intended for people who suffer from diminished hand strength and low dexterity due to non-severe forms of neuropathy or other ailments. A new variable-stiffness mechanism is designed based on the concept of aligning three different sized springs to produce four different levels of stiffness, for variable kinesthetic feedback during an exercise. Moreover, the design incorporates an interactive computer game and a flexible sensor-based glove that motivates the patients to use the exoskeleton. The patients can exercise their hands by playing the game and see their progress recorded from the glove for further motivation. Thus the rehabilitation training will be consistent and the patients will re-learn proper hand function through neuroplasticity. The developed exoskeleton is intrinsically safe when compared with active exoskeleton systems since the applied compliance provides only passive resistance. The design is also comparatively lighter than literature designs and commercial platforms.

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via An Automated Game-Based Variable-Stiffness Exoskeleton for Hand Rehabilitation – Volume 9, No. 4, April 2020 – IJMERR

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[Abstract] An EMG-controlled system combining FES and a soft exoskeleton glove for hand rehabilitation of stroke patients – Conference paper

In this paper, we present the development of a hybrid system which supports an active rehabilitation of the closing and the opening of the hand. The particularity of this system is to combine a soft exoskeleton glove, the SEM Glove™, and functional electrical stimulations (FES) to perform both types of hand movements. The created system is also a suggestion of improvement for the SEM Glove™ that is already commercialized by the BIOSERVO company and usable for hand closing rehabilitation only. In our study, a FES system was associated to this glove in order to provide the missing hand opening rehabilitation. To engage the patient in his rehabilitation, our system is electromyogram (EMG)-controlled and is activated according to the patient movement intentions. EMG signals of the muscles involved in the extension and flexion of the fingers were recorded and then processed in order to detect muscle activations. The control of the different elements of the system was then executed based on the results of this detection. The preliminary results demonstrated that the designed hybrid system shows good performances in detecting correctly the intention of a healthy user. Some improvements could still be made in the signal processing to increase the sensitivity of detection, but we proved that the hybrid system is already operational to assist the hand movements of a healthy user.

via An EMG-controlled system combining FES and a soft exoskeleton glove for hand rehabilitation of stroke patients – Danish National Research Database

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[Abstract + References] Hand rehabilitation assessment system using leap motion controller

Abstract

This paper presents an approach for monitoring exercises of hand rehabilitation for post stroke patients. The developed solution uses a leap motion controller as hand-tracking device and embeds a supervised machine learning. The K-nearest neighbor methodology is adopted for automatically characterizing the physiotherapist or helper hand movement resulting a unique movement pattern that constitutes the basis of the rehabilitation process. In the second stage, an evaluation of the patients rehabilitation exercises results is compared to the movement pattern of the patient and results are presented, saved and statistically analyzed. Physicians and physiotherapists monitor and assess patients’ rehabilitation improvements through a web application, furthermore, offer medical assisted rehabilitation processes through low cost technology, which can be easily exploited at home. Recorded tracked motion data and results can be used for further medical study and evaluating rehabilitation trends according to patient’s rehabilitation practice and improvement.

References

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[Abstract] Variable impedance control of finger exoskeleton for hand rehabilitation following stroke

Abstract

Purpose

The purpose of this paper is to propose a variable impedance control method of finger exoskeleton for hand rehabilitation using the contact forces between the finger and the exoskeleton, making the output trajectory of finger exoskeleton comply with the natural flexion-extension (NFE) trajectory accurately and adaptively.

Design/methodology/approach

This paper presents a variable impedance control method based on fuzzy neural network (FNN). The impedance control system sets the contact forces and joint angles collected by sensors as input. Then it uses the offline-trained FNN system to acquire the impedance parameters in real time, thus realizing tracking the NFE trajectory. K-means clustering method is applied to construct FNN, which can obtain the number of fuzzy rules automatically.

Findings

The results of simulations and experiments both show that the finger exoskeleton has an accurate output trajectory and an adaptive performance on three subjects with different physiological parameters. The variable impedance control system can drive the finger exoskeleton to comply with the NFE trajectory accurately and adaptively using the continuously changing contact forces.

Originality/value

The finger is regarded as a part of the control system to get the contact forces between finger and exoskeleton, and the impedance parameters can be updated in real time to make the output trajectory comply with the NFE trajectory accurately and adaptively during the rehabilitation.

 

via Variable impedance control of finger exoskeleton for hand rehabilitation following stroke | Emerald Insight

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[Abstract] Fuzzy sliding mode control of a wearable rehabilitation robot for wrist and finger

Abstract

Purpose

The purpose of this paper is to introduce a new design for a finger and wrist rehabilitation robot. Furthermore, a fuzzy sliding mode controller has been designed to control the system.

Design/methodology/approach

Following an introduction regarding the hand rehabilitation, this paper discusses the conceptual and detailed design of a novel wrist and finger rehabilitation robot. The robot provides the possibility of rehabilitating each phalanx individually which is very important in the finger rehabilitation process. Moreover, due to the model uncertainties, disturbances and chattering in the system, a fuzzy sliding mode controller design method is proposed for the robot.

Findings

With the novel design for moving the DOFs of the system, the rehabilitation for the wrist and all phalanges of fingers is done with only two actuators which are combined in one device. These features make the system a good choice for home rehabilitation. To control the robot, a fuzzy sliding mode controller has been designed for the system. The fuzzy controller does not affect the coefficient of the sliding mode controller and uses the overall error of the system to make a control signal. Thus, the dependence of the controller to the model decreases and the system is more robust. The stability of the system is proved by the Lyapunov theorem.

Originality/value

The paper provides a novel design of a hand rehabilitation robot and a controller which is used to compensate the effects of the uncertain parameters and chattering phenomenon.

via Fuzzy sliding mode control of a wearable rehabilitation robot for wrist and finger | Emerald Insight

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[Abstract] Does hand robotic rehabilitation improve motor function by rebalancing interhemispheric connectivity after chronic stroke? Encouraging data from a randomised-clinical-trial.

Abstract

OBJECTIVE:

The objective of this study was the evaluation of the clinical and neurophysiological effects of intensive robot-assisted hand therapy compared to intensive occupational therapy in the chronic recovery phase after stroke.

METHODS:

50 patients with a first-ever stroke occurred at least six months before, were enrolled and randomised into two groups. The experimental group was provided with the Amadeo™ hand training (AHT), whereas the control group underwent occupational therapist-guided conventional hand training (CHT). Both of the groups received 40 hand training sessions (robotic and conventional, respectively) of 45 min each, 5 times a week, for 8 consecutive weeks. All of the participants underwent a clinical and electrophysiological assessment (task-related coherence, TRCoh, and short-latency afferent inhibition, SAI) at baseline and after the completion of the training.

RESULTS:

The AHT group presented improvements in both of the primary outcomes (Fugl-Meyer Assessment for of Upper Extremity and the Nine-Hole Peg Test) greater than CHT (both p < 0.001). These results were paralleled by a larger increase in the frontoparietal TRCoh in the AHT than in the CHT group (p < 0.001) and a greater rebalance between the SAI of both the hemispheres (p < 0.001).

CONCLUSIONS:

These data suggest a wider remodelling of sensorimotor plasticity and interhemispheric inhibition between sensorimotor cortices in the AHT compared to the CHT group.

SIGNIFICANCE:

These results provide neurophysiological support for the therapeutic impact of intensive robot-assisted treatment on hand function recovery in individuals with chronic stroke.

 

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