[Abstract] A Robotic Hand for Rehabilitation of Wrist and Fingers

Abstract

Carpal Tunnel Syndrome is a common neural disease among people with repetitive wrist movements. It affects the sensation and movement of the thumb, index finger, middle finger, and half of the ring finger. The syndrome can be treated effectively through therapeutic exercises in the early stages of diagnosis or through surgery in more severe cases. However, the lack of physiotherapists poses a significant challenge in hospitals where thorough observation is required. The proposed rehabilitation robot focuses on wrist and finger exercises designed explicitly for Carpal Tunnel Syndrome and intended for clinical use. The sensors detect the patient’s range of motion and display the data for the therapist, who can then determine the appropriate exercises. Finger exercises include flexion-extension, mimicking grasping patterns, while wrist exercises include flexion-extension and hyperextension. The rehabilitation program consists of three stages and several iterations, progressing according to the patient’s recovery, which can be observed through the data collected from the sensors. The rehabilitation robot utilizes an under-actuation method that drives proximal interphalangeal, and distal interphalangeal joints of the four fingers using two servo motors for finger rehabilitation. The thumb is actuated using a separate servo motor. A stepper motor actuates wrist movement. A mathematical model was developed to simulate the movements of the linkage system, which is deployed in the sagittal plane of the fingers. The effectiveness of the rehabilitation robot has been verified through simulations and experiments.

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[Abstract] Preliminary Design and Finite Element Analysis of A Low-Cost Wrist Rehabilitation Robot

Abstract:

Wrist rehabilitation robots are fundamental for helping patients with stroke or wrist injuries and also decrease the workload of physiotherapists. Though wrist rehabilitation robots are essential, recent wrist rehabilitation robots have shortcomings such as heavy weight, immobility, costliness, etc. To remedy these shortcomings, in this study, we developed and produced a low-cost and mobile robotic device for these patients with partial paralysis. The device is designed to assist the patient to perform wrist exercises comfortably in the home environment without being dependent on rehabilitation centers and/or physiotherapists. Therefore, we offer the device driven by a 3D-printed mechanism in two degrees of freedom. In addition, we analyzed and simulate the robot via finite element analysis and Solidworks respectively. The results indicate the robot provides enough force, torque, and range of motion. For these reasons, the robot can be used as a compact and lighter robot in hardware and a cheaper robot in cost. So it is feasible and affordable for real-time application in wrist rehabilitation.

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[Abstract] Hand Rehabilitation System Based on Flexible Pressure Sensor – Conference Publication

Abstract:

With the development of the social economy, the demand for rehabilitation medical devices is also increasing. However, most rehabilitation devices on the market currently have problems such as bulky equipment, inconvenient operation, and high cost. Taking hand grasp force as the main monitoring parameter, this paper designs a low-cost hand rehabilitation system that is convenient to use, can remotely monitor the progress of hand rehabilitation. The system can well solve the large volume of equipment caused by the complex structure of the existing hand rehabilitation equipment. It can also solve the problems that the device cannot obtain the data of the patient’s recovery in real-time and the use cost is too high. The system includes five modules: flexible pressure sensor module, voltage conversion module, microcomputer module, LCD module and NB-IoT communication module. The flexible pressure sensor module can be attached to the patient’s hand to monitor the recovery data in real-time and see the recovery progress visually on the LCD screen. In addition, rehabilitation data can be uploaded remotely through the NB-IoT communication module for remote guidance by professional doctors to speed up the process of hand rehabilitation.

Published in: 2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT)

[Abstract] EEG-EMG hybrid real-time classification of hand grasp and release movements intention in chronic stroke patients

Abstract

Rehabilitation of the hand motor function is essential for stroke patients to resume activities of daily living. Recent studies have shown that wearable robot systems, like a multi degree-of-freedom soft glove, have the potential to improve hand motor impairment. The rehabilitation system, which is intuitively controlled according to the user’s intention, is expected to induce active participation of the user and further promote brain plasticity. However, due to the patient-specific nature of stroke patients, extracting the intention from stroke patients is still challenging. In this study, we implemented a classifier that combines EEG and EMG to detect chronic stroke patients’ four types of intention: rest, grasp, hold, and release. Three chronic stroke patients participated in the experiment and performed rest, grasp, hold, and release actions. The rest vs. grasp binary classifier and release vs. hold binary classifier showed 76.9% and 86.6% classification accuracy in real-time, respectively. In addition, patient-specific accuracy comparisons showed that the hybrid approach was robust to upper limb impairment level compared to other approaches. We believe that these results could pave the way for the development of BCI-based robotic hand rehabilitation therapy.

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[Abstract] Assistive Robotics: Robotic Trajectory Planning for Upper Extremity Rehabilitation in Patients with Hemiparesis

Abstract

Rehabilitation of upper limb movements in patients with hemiparesis has been a longstanding struggle for physical therapy professionals. Tools such as robotically assisted therapies have shown great potential to assist in the rehabilitation process of patients. Regarding traditional rehabilitation interventions, robotic systems can provide more effective and intensive physical therapy as new interventions are now being used to assist in the rehabilitation of patients with certain movement restrictions especially after stroke. Currently, this research work aims to present a study on the trajectory planning of a robotic arm, which will be suggested to the patient in order to perform the movement for rehabilitation. Furthermore, it is intended that the trajectory can be generated in real time to better meet the patient’s movement requirements and restrictions. In view of the need for real-time trajectory generation, it is necessary that a simple model of the manipulator work area is used as a boundary and boundary condition for the task space. In parallel, there is the development of a prototype of the REHABOT robot, a mechanical project developed with components from the Cyton Gamma 1500 and Reachy robots.

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[Abstract] A Control Framework for Adaptation of Training Task and Robotic Assistance for Promoting Motor Learning With an Upper Limb Rehabilitation Robot

Abstract

Robot-assisted rehabilitation has been a promising solution to improve motor learning of neurologically impaired patients. State-of-the-art control strategies are typically limited to the ignorance of heterogeneous motor capabilities of poststroke patients and therefore intervene suboptimally. In this article, we propose a control framework for robot-assisted motor learning, emphasizing the detection of human intention, generation of reference trajectories, and modification of robotic assistance. A real-time trajectory generation algorithm is presented to extract the high-level features in active arm movements using an adaptive frequency oscillator (AFO) and then integrate the movement rhythm with the minimum-jerk principle to generate an optimal reference trajectory, which synchronizes with the motion intention in the patient as well as the motion pattern in healthy humans. In addition, a subject-adaptive assistance modification algorithm is presented to model the patient’s residual motor capabilities employing spatially dependent radial basis function (RBF) networks and then combining the RBF-based feedforward controller with the impedance feedback controller to provide only necessary assistance while simultaneously regulating the maximum-tolerated error during trajectory tracking tasks. We conduct simulation and experimental studies based on an upper limb rehabilitation robot to evaluate the overall performance of the motor-learning framework. A series of results showed that the difficulty level of reference trajectories was modulated to meet the requirements of subjects’ intended motion, furthermore, the robotic assistance was compliantly optimized in response to the changing performance of subjects’ motor abilities, highlighting the potential of adopting our framework into clinical application to promote patient-led motor learning.

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[Abstract] A Virtual Assistant for Home Rehabilitation: the 2Vita-B Physical Project

Abstract

Active rehabilitation is an exercise-based program meant to help people with motor disorders improve their function. The correctness of the exercise execution has a significant impact on the effectiveness of such programs. An improperly performed exercise may impair a patient’s health. In this context, we propose the 2Vita-B Physical system. The system can be intended as a virtual assistant able – on the one hand – to support the patient during a rehabilitation exercise, and – on the other hand – to support specialists (e.g., physiotherapists) in the management of the whole rehabilitation plans. Physiotherapists have the possibility of assigning to a patient a motor rehabilitation plan, i.e., a series of rehabilitative exercises. The patient, on the other hand, can count on the support of the system itself, which allows her to perform these exercises by offering real-time feedback on the correctness of the movements that, generally, would have been provided to her by the physiotherapist. The key component of the 2Vita-B Physical systems is represented by the intelligent motion tracking and analysis component, based on both machine learning techniques and the newly released Microsoft Azure Kinect.

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[Abstract] Hand and Fingertip Detection for Game-Based Hand Rehabilitation

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Hand rehabilitation is the process of recovering hand movements to return to normal. For rehabilitation to be effective, it is necessary for patients to practice repetitive movements. During the process, the patients usually feel bored, lack motivation and enjoyable time. There is a growing interest in building games for rehabilitation to make it more interesting and motivating for patients. However, the major obstacle is that the devices are expensive, and the patients must go to use them in hospitals. This paper focuses on designing and implementing hand rehabilitation software by using hand and fingertip detection. The purpose of the project is to make the rehabilitation process more accessible, enjoyable, affordable, and can be made portable for patients to use at home. Deep learning techniques were utilized to perform hand and fingertip detection. The SuperFox game is developed; the game is controlled by commands associated with hand and fingertip movements. The system was tested with 10 participants to evaluate effectiveness of the game controls. The results showed that the system is feasible in controlling the game and able to be used to create motivation and enjoyable time for patients.

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[Abstract] A Portable Device for Hand Rehabilitation with Force Cognition: Design, Interaction and Experiment

Abstract

Introducing interactive system into portable robots for hand rehabilitation has always been a crucial topic. Moreover, hand rehabilitation with force cognition can make patients participate actively and improve rehabilitation effect. In this paper, we design a portable robotic device with interactive system for patients to rehabilitate with force cognition. Firstly, an exoskeleton glove is designed with a compact mechanical structure which is controlled by a real-time feedback system. The portable device allows patients to rehabilitate not only in hospital. Next, an interactive system including virtual environment and force cognition is introduced to detect the hand motion and collision. At last, clinical tests of our portable device is carried out with 9 subjects after tendon injury to show the effective assistance with our device.

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[Abstract] An Electromyography-aided Robotics Hand for Rehabilitation – A Proof-of-Concept Study

Abstract

The conventional rehabilitation process has benefited in aiding a patient’s recovery significantly. However, for a typical traditional rehabilitation session to occur, a medical professional is required to present alongside the patient. With the advancement of technology, robotics is now being integrated into the rehabilitation process to provide significant benefits to the rehabilitation environment. The objective of this project is to develop a Rehabilitative Robotics System that assists stroke patients with the recovery of their hand movements. A hand exoskeleton is integrated with an Electromyography (EMG) sensor, a linear actuator, a microcontroller, and a screen. The EMG input is used to detect a patient’s intention to open or close his/her arm with the assistance of the linear actuator. The screen will display the options which allow the patient to select the mode of the rehabilitation process. The results of the session are then recorded in the computer for further analysis by the medical professionals. The system is aimed to design a portable, lightweight device that can be mobile with the patient without a secondary assistant. This paper presents the literature review alongside the development of the hand exoskeleton design, hardware components and design, software implementation, and system implementations.

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