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.
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.
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.
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.
This study investigates the effect of combining both mirror therapy with Electrical Stimulation (ES) on improvement of the function of lower extremity compared to conventional therapy. 18 stroke survivors (sub acute stage) were recruited, 9 of them were randomly assigned to receive conventional treatment and another 9 started the mirror therapy combined with ES treatment. Duration of each session in both interventions was 50 minutes, done 4 times per week over two weeks. After 2 weeks, subjects took one week rest before switching they type of treatment; those started with conventional therapy continued with mirror therapy combined with ES, and vice versa. The duration of this phase was 2 weeks with same schedule as the 1st one. Ankle dorsi-flexion range of motion, lower extremity sensory-motor function, and walking duration were measured at baseline, after 1st 2 weeks, and immediately after the last two weeks, and 4 weeks after end of training (retention test). Repeated Measures ANCOVA was done to compare outcome measures scores in both groups and between all testing days, and paired T-test was used measure the difference between groups. Significant increase in all outcome measures was found after the (MT+ES) training, which is higher than conventional therapy training (p<;0.0001). In conclusion, the results suggest that combination of mirror therapy and ES is more effective than conventional therapy in improving lower limb motor function after stroke.
Stroke is a sudden loss of the blood supply to brain tissues where a focal neurological disturbance of brain function rapidly develops. The symptoms of stroke last more than 24 hours and depend on the area of the brain that has been affected. Lower-extremity motor function after stroke is often impaired, causing restrictions in function, gait, and postural performance . Because ankle is one of the most important joints in gait, especially related to dorsiflexion movement , the gait performance is highly diminished as a result of ankle movement impairment . Recovery is most prominent within the first three to six months after stroke. Thus, implementation of intensive therapy within this duration post stroke can lead to faster improvement in activities . Conventional treatment approaches (like Brunnstrom’s approach or Bobath’s approach) for hemiplegic patients have been used for many years, even though they are not always evidence-based and their neurophysiologic background is poorly investigated. On the other hand, several promising rehabilitation approaches have been recently developed addressing the motor recovery and balance of lower extremity in stroke; such as virtual reality, mental imagery, robotic interactive therapy, electrical stimulation, and mirror therapy .
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.
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 . Stroke can lead to the dysfunction of the brain central nervous, often characterized by language, cognitive or motor dysfunction , . The medical rehabilitation mechanism of stroke is based on neural plasticity theory and the theory of mirror neurons .
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.
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.
Recent neural science research suggests that a robotic device can be an effective tool to deliver the repetitive movement training that is needed to trigger neuroplasticity in the brain following neurologic injuries such as stroke and spinal cord injury (SCI).
In such scenario, adaptive control of the robotic device to provide assistance as needed along the intended motion trajectory with exact amount of force intensity, though complex, is a more effective approach. A critic-actor based reinforcement learning neural network (RLNN) control method is explored to provide adaptive control during post-stroke fine hand motion rehabilitation training.
The effectiveness of the method is verified through computer simulation and implementation on a hand rehabilitation robotic device.
Results suggest that the control system can fulfil the assist-as-needed (AAN) control with high performance and reliability. The method demonstrates potential to encourage active participation of the patient in the rehabilitation process and to improve the efficiency of the process.
Many stroke patients and elderly have a reduced hand function, resulting in difficulties with independently performing activities of daily living (ADL). Assistive technology is a promising alternative to support the upper limb in performing ADL. To avoid device abandonment, end-users should be involved early in the design and development phase to identify user requirements for assistive technology.
The present study applies a user-centred approach to identify user requirements for wearable soft-robotic gloves targeted at physical support of hand function during ADL for elderly and stroke patients.
Elderly, stroke patients and healthcare professionals, participating in focus groups, specified requirements regarding:
activities that need support of assistive technology,
design of wearable robotic devices for hand support, and
application of assistive technology as training tool at home.
Assistive technology for the support of the hand is considered valuable by users for assisting ADL, but only if the device is wearable, compact, lightweight, easy to use, quickly initialized, washable and only supports the particular function(s) that an individual need(s) assistance with, without taking over existing function(s) from the user.