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
Nowadays virtual reality (VR) technology give us the considerable opportunities to develop new methods to supplement traditional physiotherapy with sustain beneficial quantity and quality of rehabilitation. VR tools, like Leap motion have received great attention in the recent few years because of their immeasurable applications, whish include gaming, robotics, education, medicine etc. In this paper we present a game for hand rehabilitation using the Leap Motion controller. The main idea of gamification of hand rehabilitation is to help develop the muscle tonus and increase precision in gestures using the opportunities that VR offer by making the rehabilitation process more effective and motivating for patients.
Source: Gamification of Hand Rehabilitation Process Using Virtual Reality Tools: Using Leap Motion for Hand Rehabilitation – IEEE Xplore Document
The objective of this work was to design and experiment a robotic hand rehabilitation device integrated with a wireless EEG system, going towards patient active participation maximization during the exercise. This has been done through i) hand movement actively triggered by patients muscular activity as revealed by electromyographic signals (i.e., a target hand movement for the rehabilitation session is defined, the patient is required to start the movement and only when the muscular activity overcomes a predefined threshold, the patient-initiated movement is supported); ii) an EEG-based biofeedback implemented to make the user aware of his/her level of engagement (i.e., brain rhythms power ratio Beta/Alpha). The designed system is composed by the Gloreha hand rehabilitation glove, a device for electromyographic signals recording, and a wireless EEG headset. A strong multidisciplinary approach was the base to reach this goal, which is the fruitful background of the Think and Go project. Within this project, research institutes (Politecnico di Milano), clinical centers (INRCA-IRCCS), and companies (ab medica s.p.a., Idrogent, SXT) have worked together throughout the development of the integrated robotic hand rehabilitation device. The integrated device has been tested on a small pilot group of healthy volunteers. All the users were able to calibrate and correctly use the system, and they reported that the system was more challenging to be used with respect to the standard passive hand mobilization session, and required more attention and involvement. The results obtained during the preliminary tests are encouraging, and demonstrate the feasibility of the proposed approach.
Source: Technical validation of an integrated robotic hand rehabilitation device: Finger independent movement, EMG control, and EEG-based biofeedback – IEEE Xplore Document
This paper describes the design and initial prototype of a thumb curling exoskeleton for movement therapy. This add-on device for the Finger INdividuating Grasp Exercise Robot (FINGER) guides the thumb through a single-degree-of-freedom naturalistic grasping motion. This motion complements the grasping motions of the index and middle fingers provided by FINGER. The kinematic design and mechanism synthesis described herein utilized 3D motion capture and included the determination of the principle plane of the thumb motion for the simple grasping movement. The results of the design process and the creation of a first prototype indicate that this thumb module for finger allows naturalistic thumb motion that expands the capabilities of the FINGER device.
Source: IEEE Xplore Document – Design of a thumb module for the FINGER rehabilitation robot
Stroke is one of the leading causes of disability worldwide. Consequently, many stroke survivors exhibit difficulties undergoing voluntary movement in their affected upper limb, compromising their functional performance and level of independence. To minimize the negative impact of stroke disabilities, exercises are recognized as a key element in post-stroke rehabilitation.
In order to provide the practice of exercises in a uniform and controlled manner as well as increasing the efficiency of therapists’ interventions, robotic training has been found, and continues to prove itself, as an innovative intervention for post-stroke rehabilitation. However, the complexity as well as the limited degrees of freedom and workspace of currently commercially available robots can limit their use in clinical settings. Up to now, user-friendly robots covering a sufficiently large workspace for training of the upper limb in its full range of motion are lacking.
This paper presents the design and implementation of ERA, an upper-limb 3-DOF force-controlled exerciser robot, which presents a workspace covering the entire range of motion of the upper limb. The ERA robot provides 3D reaching movements in a haptic virtual environment. A description of the hardware and software components of the ERA robot is also presented along with a demonstration of its capabilities in one of the three operational modes that were developed.
Source: IEEE Xplore Document – Exerciser for rehabilitation of the Arm (ERA): Development and unique features of a 3D end-effector robot
One of the recent trends in gait rehabilitation is to incorporate bio-signals, such as electromyography (EMG) or electroencephalography (EEG), for facilitating neuroplasticity, i.e. top-down approach. In this study, we investigated decoding stroke patients’ gait intention through a wireless EEG system. To overcome patient-specific EEG patterns due to impaired cerebral cortices, common spatial patterns (CSP) was employed. We demonstrated that CSP filter can be used to maximize the EEG signal variance-ratio of gait and standing conditions. Finally, linear discriminant analysis (LDA) classification was conducted, whereby the average accuracy of 73.2% and the average delay of 0.13 s were achieved for 3 chronic stroke patients. Additionally, we also found out that the inverse CSP matrix topography of stroke patients’ EEG showed good agreement with the patients’ paretic side.
Source: IEEE Xplore Document – Detecting voluntary gait intention of chronic stroke patients towards top-down gait rehabilitation using EEG
Many mechatronic devices exist to facilitate hand rehabilitation, however few directly address deficits in muscle activation patterns while also enabling functional task practice.
We developed an innovative voice and electromyography-driven actuated (VAEDA) glove, which is sufficiently flexible/portable for incorporation into hand-focused therapy post-stroke. The therapeutic benefits of this device were examined in a longitudinal intervention study. Twenty-two participants with chronic, moderate hand impairment (Chedoke-McMaster Stroke Assessment Stage of Hand (CMSA-H=4)) enrolled >8 months post-stroke for 18 one-hour training sessions (3x/week) employing a novel hand-focused occupational therapy paradigm, either with (VAEDA) or without (No-VAEDA) actuated assistance.
Outcome measures included CMSA-H, Wolf Motor Function Test (WMFT), Action Research Arm Test, Fugl-Meyer Upper Extremity Motor Assessment (FMUE), grip and pinch strength and hand kinematics. All outcomes were recorded at baseline and endpoint (immediately after and 4 weeks post-training).
Significant improvement was observed following training for some measures for the VAEDA group (n=11) but for none of the measures for the No-VAEDA group (n=11). Specifically, statistically significant gains were observed for CMSA-H (p=0.038) and WMFT (p=0.012) as well as maximum digit aperture subset (p=0.003, n=7), but not for the FMUE or grip or pinch strengths.
In conclusion, therapy effectiveness appeared to be increased by employment of the VAEDA glove, which directly targets deficits in muscle activation patterns.
Source: IEEE Xplore Abstract (Abstract) – Benefits of using a voice and EMG-driven actuated glove to support occupational therapy for stroke s…
21-23 Oct. 2015
In the last two decades, robot-aided rehabilitation has become widespread, particularly for upper limb movement rehabilitation. In this Doctoral Consortium I present a system for physical and cognitive rehabilitation that uses a combination of Serious Games to allow the monitoring and progress tracking of a person during physical therapy. The system records physical and cognitive states through the interaction with the advance robotic arm in order to assess the users hand-eye coordination, response interaction, working memory and concentration rates.
Source: IEEE Xplore Abstract (Abstract) – Robot-Assisted Rehabilitation for Smart Assessment and Training
Virtual reality therapy systems have the potential to increase the intensity and frequency of physical activity of stroke patients at home. This might help to increase the dose of rehabilitation, without the costs associated with clinic visits and therapist supervision.
We present a therapy game that continuously estimates the patient’s arm reachable three-dimensional (3D) workspace with a voxel-based model and selects targets to be reached accordingly, in order to increase challenge without causing frustration. This exercise is implemented on a novel, inertial measurement unit (IMU) based virtual reality system for the training of upper limb function. We present data from a pilot trial with 5 chronic stroke patients who trained for 6 weeks at home and without therapist supervision.
On average, the patients’ in-game assessed 3D workspace grew by 10.7% in volume and their score on the Fugl-Meyer Upper Extremity score improved by 5 points. The average self-selected therapy time, over the course of the therapy, was 16.8 h. These results suggest that the proposed assessment-driven target selection is viable for unsupervised home therapy and could form the basis for additional therapy games in the future.
Source: IEEE Xplore Abstract – Assessment-driven arm therapy at home using an IMU-based virtual reality system
A knowledge gap exists for how to improve hand rehabilitation after stroke using robotic rehabilitation methods, and non-robotic hand rehabilitation methods show only small patient improvements. A proposed solution for this knowledge gap is to integrate the strengths of three of the most favorable rehabilitation strategies for post-stroke rehabilitation of hand function, which are constraint-induced movement therapy (CIMT), high-intensity therapy, and repetitive task training, with a robotic rehabilitation gaming system.
To create a system that is composed of collaborative therapy efforts, we must first understand how to encourage rehabilitation intervention motions. An experiment was conducted in which healthy participants were asked to complete six levels of a rehabilitation game, each level designed to encourage a specific therapeutic intervention, and a control, where participants were asked to complete undefined exercise motions.
The results showed that participants’ motions were significantly different than the control while playing each of the levels. Upon comparing the actual paths of participants to the paths encouraged by the levels, it was discovered that the participants followed the intended path while encouragement was being provided for them to do so. When the encouraged motions required quick, hard motions, the participants would follow an aliased version of the intended path.
This study suggests that robotic rehabilitation systems can not only change how a participant moves, but also encourage specific motions designed to mimic therapeutic interventions.
via IEEE Xplore Abstract (Abstract) – Encouraging specific intervention motions via a robotic system for rehabilitation of hand function: ….