Posts Tagged Training
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.
[Abstract] A novel scheme of finger recovery based on symmetric rehabilitation: Specially for hemiplegia
[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] Mirror therapy combined with functional electrical stimulation for rehabilitation of stroke survivors’ ankle dorsiflexion
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 .
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 .
[Abstract] Exerciser for rehabilitation of the Arm (ERA): Development and unique features of a 3D end-effector robot
[Abstract] Benefits of using a voice and EMG-driven actuated glove to support occupational therapy for stroke survivors.
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.
[ARTICLE] Reinforcement learning neural network (RLNN) based adaptive control of fine hand motion rehabilitation robot.
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.
[ARTICLE] User-centred input for a wearable soft-robotic glove supporting hand function in daily life
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.
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.