Posts Tagged Training

[Abstract] The eWrist — A wearable wrist exoskeleton with sEMG-based force control for stroke rehabilitation.

Abstract:

Chronic wrist impairment is frequent following stroke and negatively impacts everyday life. Rehabilitation of the dysfunctional limb is possible but requires extensive training and motivation. Wearable training devices might offer new opportunities for rehabilitation. However, few devices are available to train wrist extension even though this movement is highly relevant for many upper limb activities of daily living. As a proof of concept, we developed the eWrist, a wearable one degree-of-freedom powered exoskeleton which supports wrist extension training. Conceptually one might think of an electric bike which provides mechanical support only when the rider moves the pedals, i.e. it enhances motor activity but does not replace it. Stroke patients may not have the ability to produce overt movements, but they might still be able to produce weak muscle activation that can be measured via surface electromyography (sEMG). By combining force and sEMG-based control in an assist-as-needed support strategy, we aim at providing a training device which enhances activity of the wrist extensor muscles in the context of daily life activities, thereby, driving cortical reorganization and recovery. Preliminary results show that the integration of sEMG signals in the control strategy allow for adjustable assistance with respect to a proxy measurement of corticomotor drive.

Source: The eWrist — A wearable wrist exoskeleton with sEMG-based force control for stroke rehabilitation – IEEE Xplore Document

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[Abstract] Applying a soft-robotic glove as assistive device and training tool with games to support hand function after stroke: Preliminary results on feasibility and potential clinical impact

Published in: Rehabilitation Robotics (ICORR), 2017 International Conference on

Abstract:

Recent technological developments regarding wearable soft-robotic devices extend beyond the current application of rehabilitation robotics and enable unobtrusive support of the arms and hands during daily activities. In this light, the HandinMind (HiM) system was developed, comprising a soft-robotic, grip supporting glove with an added computer gaming environment. The present study aims to gain first insight into the feasibility of clinical application of the HiM system and its potential impact. In order to do so, both the direct influence of the HiM system on hand function as assistive device and its therapeutic potential, of either assistive or therapeutic use, were explored. A pilot randomized clinical trial was combined with a cross-sectional measurement (comparing performance with and without glove) at baseline in 5 chronic stroke patients, to investigate both the direct assistive and potential therapeutic effects of the HiM system. Extended use of the soft-robotic glove as assistive device at home or with dedicated gaming exercises in a clinical setting was applicable and feasible. A positive assistive effect of the soft-robotic glove was proposed for pinch strength and functional task performance ‘lifting full cans’ in most of the five participants. A potential therapeutic impact was suggested with predominantly improved hand strength in both participants with assistive use, and faster functional task performance in both participants with therapeutic application.

Source: Applying a soft-robotic glove as assistive device and training tool with games to support hand function after stroke: Preliminary results on feasibility and potential clinical impact – IEEE Xplore Document

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[Abstract] Design factors and opportunities of rehabilitation robots in upper-limb training after stroke

Abstract:

The occurrence of strokes has been progressively increasing. Upper limb recovery after stroke is more difficult than lower limb. One of the rapidly expanding technologies in post-stroke rehabilitation is robot-aided therapy. The advantage of robots is that they are able to deliver highly repetitive therapeutic tasks with minimal supervision of a therapist. However, from the literature, the focus of robotic design in stroke rehabilitation has been technology-driven. Clinical and therapeutic requirements were not seriously considered in the design of rehabilitation robots. The purpose of this study was twofold: (1) demonstrate the missing elements of current robot-aided therapy; (2) identify design factors and opportunities of rehabilitation robots (in upper-limb training after stroke). In this study, we performed a literature review on articles relevant to rehabilitation robots in upper-limb training after stroke. We identified the design foci of current rehabilitation robots for upper limb stroke recovery. Using the therapeutic framework for stroke rehabilitation in occupational therapy, we highlighted design factors and opportunities of rehabilitation robots. The outcomes of this study benefit the robotics design community in the design of rehabilitation robots.

1. Introduction

A robot is defined as a machine programmable to perform and modify tasks in response to changes in the environment [1]. The benefits of robots are noticeable in productivity, safety, and in saving time and money. The advancement of robot technologies in the past decade caused the wide adoption of robots in our lives and in the society. For instance, in education, robots were implemented in undergraduate courses to teach core artificial intelligence concepts, e.g., algorithms for searching tree data structures [2]. In agriculture, robotic milking systems (being able to reduce labor/operational costs) were installed to replace conventional milking that gave cows the freedom to be milked throughout the day [3]. In healthcare, service robots were implemented to provide functional assistance for the elderly in home environments, e.g., bringing medication for the emergency and picking up heavy objects low on the ground [4].

Source: Design factors and opportunities of rehabilitation robots in upper-limb training after stroke – IEEE Xplore Document

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[Abstract] Portable and Reconfigurable Wrist Robot Improves Hand Function for Post-Stroke Subjects  

Abstract:

Rehabilitation robots have become increasingly popular for stroke rehabilitation. However, the high cost of robots hampers their implementation on a large scale. This study implements the concept of a modular and reconfigurable robot, reducing its cost and size by adopting different therapeutic end effectors for different training movements using a single robot. The challenge is to increase the robot’s portability and identify appropriate kinds of modular tools and configurations. Because literature on the effectiveness of this kind of rehabilitation robot is still scarce, this paper presents the design of a portable and reconfigurable rehabilitation robot and describes its use with a group of post-stroke patients for wrist and forearm training. Seven stroke subjects received training using a reconfigurable robot for 30 sessions, lasting 30 minutes per session. Post-training, statistical analysis showed significant improvement of 3.29 points (16.20%, p = 0.027) on the Fugl-Meyer Assessment Scale for forearm and wrist components (FMA-FW). Significant improvement of active range of motion (AROM) was detected in both pronation-supination (75.59%, p = 0.018) and wrist flexion-extension (56.12%, p = 0.018) after the training. These preliminary results demonstrate that the developed reconfigurable robot could improve subjects’ wrist and forearm movement.

Source: Portable and Reconfigurable Wrist Robot Improves Hand Function for Post-Stroke Subjects – IEEE Xplore Document

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[BOOK + References] Adaptation and Rehabilitation in Patients with Homonymous Visual Field Defects

Abstract

Hemianopia leads to severe impairment of spatial orientation and mobility. In cases without macular sparing an additional reading disorder occurs. Persistent visual deficits require rehabilitation. The goal is to compensate for the deficits to regain independence and to maintain the patient’s quality of life. Spontaneous adaptive mechanisms, such as shifting the field defect towards the hemianopic side by eye movements or eccentric fixation, are beneficial, but often insufficient. They can be enhanced by training, e.g., saccadic training to utilize the full field of gaze in order to improve mobility and by special training methods to improve reading performance. At present only compensatory interventions are evidence-based.

References (71)

Source: Adaptation and Rehabilitation in Patients with Homonymous Visual Field Defects – Springer

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[Abstract] An extended kinematic model for arm rehabilitation training and assessment

Abstract

In the rehabilitation training and assessment of upper limbs, the conventional kinematic model treats the arm as a serial manipulator and maps the rotations in the joint space to movements in the Cartesian space. While this model brings simplicity and convenience, and thus has been overwhelming used, its accuracy is limited, especially for the distal parts of the upper limb that execute dexterous movements.

In this paper, a novel kinematic model of the arm has been proposed, which has been inspired by the biomechanical analysis of the forearm and wrist anatomy. One additional parameter is introduced into the conventional arm model, and then both the forward and inverse kinematic models of five parameters are derived for the motion of upper arm medial/lateral rotation, elbow flexion/extension, forearm pronation/supination, wrist flexion/extension and ulnar/radial deviation. Then, experiments with an advanced haptic interface have been designed and performed to examine the presented arm kinematic model. Data analysis revealed that accuracy and robustness can be significantly improved with the new model.

This extended arm kinematic model will help device development, movement training and assessment of upper limb rehabilitation.

Published in: Advanced Robotics and Mechatronics (ICARM), International Conference on

Source: An extended kinematic model for arm rehabilitation training and assessment – IEEE Xplore Document

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[Abstract] A novel scheme of finger recovery based on symmetric rehabilitation: Specially for hemiplegia

Abstract:

Finger recovery is much harder than other parts on the upper limbs, because finger recovery movement has several key problems need to overcome, including high precision of movement, high control resolution requirements, variable data with different person, as well as the fuzzy signal during the movement. In order to overcome the difficulties, a new scheme of finger recovery is presented in the paper based on symmetric rehabilitation. In the paralyzed hand side, a mechanical exoskeleton hand is designed and simulated to provide skeletal traction, while in the regular hand side, the curve magnitude of every joint during movement is detected. Then the hand motion is analyzed and recognized using Multi-class SVM. Many candidates were chosen to perform the experiment, and the data produced by the candidates were divided the training parts and recognition parts. Experiments shows that the Multi-class SVM is effective and practical for classification and recognition, and could be helpful in the finger recovery process.

Source: A novel scheme of finger recovery based on symmetric rehabilitation: Specially for hemiplegia – IEEE Xplore Document

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[Abstract] Design and Test of a Closed-Loop FES System for Supporting Function of the Hemiparetic Hand Based on Automatic Detection using the Microsoft Kinect sensor

Abstract:

This paper describes the design of a FES system automatically controlled in a closed loop using a Microsoft Kinect sensor, for assisting both cylindrical grasping and hand opening. The feasibility of the system was evaluated in real-time in stroke patients with hand function deficits. A hand function exercise was designed in which the subjects performed an arm and hand exercise in sitting position. The subject had to grasp one of two differently sized cylindrical objects and move it forward or backwards in the sagittal plane. This exercise was performed with each cylinder with and without FES support. Results showed that the stroke patients were able to perform up to 29% more successful grasps when they were assisted by FES. Moreover, the hand grasp-and-hold and hold-and-release durations were shorter for the smaller of the two cylinders. FES was appropriately timed in more than 95% of all trials indicating successful closed loop FES control. Future studies should incorporate options for assisting forward reaching in order to target a larger group of stroke patients.

Source: Design and Test of a Closed-Loop FES System for Supporting Function of the Hemiparetic Hand Based on Automatic Detection using the Microsoft Kinect sensor – IEEE Xplore Document

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[Abstract] A motion rehabilitation self-training and evaluation system using Kinect

Abstract:

Stroke patients usually have difficulties to conduct rehabilitation training themselves, due to no rehabilitation evaluation in time and dependence on doctors. In order to solve this problem, this paper proposes a motion rehabilitation and evaluation system based on the Kinect gesture measuring technology combining VR technology as well as traditional method of stroke rehabilitation. Real-time rehabilitation motion feedback is achieved by using Kinect motion capturing, customized skeleton modeling, and virtual characters constructed in Unity3D. The jitter problem of virtual characters following motion using Kinect is solved. Fidelity and interactivity of virtual rehabilitation training is improved. Our experiment validated the feasibility of this system preliminarily.

1. Introduction

With an ageing population problem increasingly prominent, the number of hemiplegia patients is growing caused by stroke, which has a high morbidity and high mortality rate [1]. Stroke can lead to the dysfunction of the brain central nervous, often characterized by language, cognitive or motor dysfunction [2], [3]. The medical rehabilitation mechanism of stroke is based on neural plasticity theory and the theory of mirror neurons [4].

Source: A motion rehabilitation self-training and evaluation system using Kinect – IEEE Xplore Document

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