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Posts Tagged Fingers
Cloud-based rehabilitation services for post-stroke hand disability.
Tensor-based pattern recognition technique to detect the real-time condition of patient.
The integration of cloud computing with AR-based rehabilitation system.
Multi-sensory big data oriented tensor approach to handle patient’s collected data.
Given the flexibility and potential of cloud technologies, cloud-based rehabilitation frameworks have shown encouraging results as assistive tools for post-stroke disability rehabilitation exercises and treatment. To treat post-stroke disability, cloud-based rehabilitation offers great advantages over conventional, clinic-based rehabilitation, providing ubiquitous flexible rehabilitation services and storage while offering therapeutic feedback from a therapist in real-time during patients’ rehabilitative movements. With the development of sensory technologies, cloud computing technology integrated with Augmented Reality (AR) may make therapeutic exercises more enjoyable.
To achieve these objectives, this paper proposes a framework for cloud-based rehabilitation services, which uses AR technology along with other sensory technologies. We have designed a prototype of the framework that uses the mechanism of sensor gloves to recognize gestures, detecting the real-time condition of a patient doing rehabilitative exercises. This prototype framework is tested on twelve patients not using sensor gloves and on four patients wearing sensor gloves over six weeks. We found statistically significant differences between the forces exerted by patients’ fingers at week one compared to week six. Significant improvements in finger strength were found after six weeks of therapeutic rehabilitative exercises.
[ARTICLE] Development of the Wrist Rehabilitation Therapy (WRist-T) Device based on Automatic Control for Traumatic Brain Injury Patient – iMEDiTEC 2017 – Full Text
In Malaysia, there are not many physiotherapists (PT) as well as rehabilitation centers. Limb rehabilitation is common in rehabilitation centers which include upper limbs and lower limbs. Generally, for upper limb, wrist, hand and fingers rehabilitation is frequently conducted in the centers by PT. The current scenario in Malaysia for wrist rehabilitation is the PT use conventional method to carry out the rehabilitation
procedures. The problem with this procedures, it is time-consuming as the PT need to attend every patient for about 20-30 minutes. This could also lead to exhaustion both to PT and patients. The session can only be done with the assistance on PT, however, there are many patients could not commit to the therapy session due to logistic and domestic problems. This problem can be greatly solved with rehabilitation robot but the
current product in the market is expensive and not affordable especially for low-income earners family. In this paper, a novel automatic control of wrist rehabilitation therapy; called WRist-T device has been developed. The novelty of the device is three modes of exercises that can be carried out which is the flexion and extension, radial and ulnar deviation and pronation and supination. By using this device, the patient can easily receive physiotherapy session with minor supervision from the physiotherapist at the hospital or rehabilitation center and also can be conducted at patient home.[…]
[Abstract] Quantification method of motor function recovery of fingers by using the device for home rehabilitation – IEEE Conference Publication
[Conference paper] FEX a Fingers Extending eXoskeleton for Rehabilitation and Regaining Mobility – Abstract+References
This paper presents the design process of an exoskeleton for executing human fingers’ extension movement for the rehabilitation procedures and as an active orthosis purposes. The Fingers Extending eXoskeleton (FEX) is a serial, under-actuated mechanism capable of executing fingers’ extension. The proposed solution is easily adaptable to any finger length or position of the joints. FEX is based on the state-of-art FingerSpine serial system. Straightening force is transmitted from a DC motor to the exoskeleton structures with use of pulled tendons. In trial tests the device showed good usability and functionality. The final prototype is a result of almost half a year of the development process described in this paper.
[Abstract+References] Toward wearable supernumerary robotic fingers to compensate missing grasping abilities in hemiparetic upper limb
This paper presents the design, analysis, fabrication, experimental characterization, and evaluation of two prototypes of robotic extra fingers that can be used as grasp compensatory devices for a hemiparetic upper limb. The devices are the results of experimental sessions with chronic stroke patients and consultations with clinical experts. Both devices share a common principle of work, which consists in opposing the device to the paretic hand or wrist so to restrain the motion of an object. They can be used by chronic stroke patients to compensate for grasping in several activities of daily living (ADLs) with a particular focus on bimanual tasks. The robotic extra fingers are designed to be extremely portable and wearable. They can be wrapped as bracelets when not being used, to further reduce the encumbrance. Both devices are intrinsically compliant and driven by a single actuator through a tendon system. The motion of the robotic devices can be controlled using an electromyography-based interface embedded in a cap. The interface allows the user to control the device motion by contracting the frontalis muscle. The performance characteristics of the devices have been measured experimentally and the shape adaptability has been confirmed by grasping various objects with different shapes. We tested the devices through qualitative experiments based on ADLs involving five chronic stroke patients. The prototypes successfully enabled the patients to complete various bimanual tasks. Results show that the proposed robotic devices improve the autonomy of patients in ADLs and allow them to complete tasks that were previously impossible to perform.
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Source: Toward wearable supernumerary robotic fingers to compensate missing grasping abilities in hemiparetic upper limbThe International Journal of Robotics Research – Irfan Hussain, Giovanni Spagnoletti, Gionata Salvietti, Domenico Prattichizzo, 2017
[ARTICLE] A Neuromuscular Electrical Stimulation (NMES) and robot hybrid system for multi-joint coordinated upper limb rehabilitation after stroke – Full Text
It is a challenge to reduce the muscular discoordination in the paretic upper limb after stroke in the traditional rehabilitation programs.
In this study, a neuromuscular electrical stimulation (NMES) and robot hybrid system was developed for multi-joint coordinated upper limb physical training. The system could assist the elbow, wrist and fingers to conduct arm reaching out, hand opening/grasping and arm withdrawing by tracking an indicative moving cursor on the screen of a computer, with the support from the joint motors and electrical stimulations on target muscles, under the voluntary intention control by electromyography (EMG). Subjects with chronic stroke (n = 11) were recruited for the investigation on the assistive capability of the NMES-robot and the evaluation of the rehabilitation effectiveness through a 20-session device assisted upper limb training.
In the evaluation, the movement accuracy measured by the root mean squared error (RMSE) during the tracking was significantly improved with the support from both the robot and NMES, in comparison with those without the assistance from the system (P < 0.05). The intra-joint and inter-joint muscular co-contractions measured by EMG were significantly released when the NMES was applied to the agonist muscles in the different phases of the limb motion (P < 0.05). After the physical training, significant improvements (P < 0.05) were captured by the clinical scores, i.e., Modified Ashworth Score (MAS, the elbow and the wrist), Fugl-Meyer Assessment (FMA), Action Research Arm Test (ARAT), and Wolf Motor Function Test (WMFT).
The EMG-driven NMES-robotic system could improve the muscular coordination at the elbow, wrist and fingers.
Stroke is a main cause of long-term disability in adults . Approximately 70 to 80% stroke survivors experienced impairments in their upper extremity, which greatly affects the independency of their daily living [2, 3]. In the upper limb rehabilitation, it also has been found that the recovery of the proximal joints, e.g., the shoulder and the elbow, is much better than the distal, e.g., the wrist and fingers [4, 5]. The main possible reasons are: 1) The spontaneous motor recovery in early stage after stroke is from the proximal to the distal; and 2) the proximal joints experienced more effective physical practices than the distal joints throughout the whole rehabilitation process, since the proximal joints are easier to be handled by a human therapist and are more voluntarily controllable by most of stroke survivors . However, improved proximal functions in the upper limb without the synchronized recovery at the distal makes it hard to apply the improvements into meaningful daily activities, such as reaching out and grasping objects, which requires the coordination among the joints of the upper limb, including the hand. More effective rehabilitation methods which may benefit the functional restoration at both the proximal and the distal are desired for post-stroke upper limb rehabilitation.
Besides the weakness and spasticity of muscles in the paretic upper limb, discoordination among muscles is also one of the major impairments after stroke, mainly reflected as abnormal muscular co-activating patterns and loss of independent joint control [2, 6]. Stereotyped movements of the entire limb with compensation from the proximal joints are commonly observed in most of persons with chronic stroke who have passed six months after the onset of the stroke, during which abnormal motor synergies were gradually developed. Neuromuscular electrical stimulation (NMES) is a technique that can generate limb movements by applying electrical current on the paretic muscles . Post-stroke rehabilitation assisted with NMES has been found to effectively prevent muscle atrophy and improve muscle strength , and the stimulation also evokes sensory feedback to the brain during muscle contraction to facilitate motor relearning . It has been found that NMES can improve muscular coordination in a paralysed limb by limiting ‘learned disuse’ that stroke survivors are gradually accustomed to managing their daily activities without using certain muscles, which has been considered as a significant barrier to maximizing the recovery of post-stroke motor function . However, difficulties have been found in NMES alone to precisely activate groups of muscles for dynamic and coordinated limb movements with desired accuracy in kinematics, for example, speeds and trajectories. It is because most of the NMES systems adopted transcutaneous stimulation with surface electrodes only recruiting muscles located closely to the skin surface with limited stimulation channels . Therefore, the muscular force evoked may not be enough to achieve the precise limb motions. However, limb motions with repeated and close-to-normal kinematic experiences are necessary to enhance the sensorimotor pathways in rehabilitation, which has been found to contribute to the motor recovery after stroke . Furthermore, faster muscular fatigue would be experienced when using NMES with intensive stimuli, in comparison with the muscle contraction by biological neural stimulation .
The use of rehabilitation robots is one of the solutions to the shortage of affordable professional manpower in the industry of physical therapy, to cope with the long-term and labour-demanding physical practices . In comparison with the NMES, robots can well control the limb movements with electrical motors. Various robots have been proposed for upper limb training after stroke [12, 13]. Among them, the robots with the involvement of voluntary efforts from persons after stroke demonstrated better rehabilitation effects than those with passive limb motions, i.e., the limb movements are totally dominated by the robots . Physical training with passive motions only contributed to the temporary release of muscle spasticity; whereas, voluntary practices could improve the motor functions of the limb with longer sustainability [10, 14]. In our previous studies, we designed a series of voluntary intention-driven rehabilitation robotics for physical training at the elbow, the wrist and fingers [14, 15, 16, 17, 18]. Residual electromyography (EMG) from the paretic muscles was used to control the robots to provide assistive torques to the limb for desired motions. The results of applying these robots in post-stroke physical training showed that the target joint could obtain motor improvements after the training; however, more significant improvements usually appeared at its neighbouring proximal joint mainly due to the compensatory exercises from the proximal muscles [15, 17]. In order to improve the muscle coordination during robot-assisted training, we integrated NMES into the EMG-driven robot as an intact system for wrist rehabilitation [16, 19]. It has been found that the combined assistance with both robot and NMES could reduce the excessive muscular activities at the elbow and improve the muscle activation levels related to the wrist, which was absent in the pure robot assisted training . More recently, combined treatment with robot and NMES for the wrist by other research group also demonstrated more promising rehabilitation effectiveness in the upper limb functions than pure robot training . However, most of the proposed devices are for single joint treatment, and cannot be used for multi-joint coordinated upper limb training. Furthermore, the training tasks provided by these devices are not easy to be directly translated into daily activities. We hypothesized that multi-joint coordinated upper limb training assisted by both NMES and robot could improve the muscular coordination in the whole upper limb and promote the synchronized recovery at both the proximal and distal joints. In this work, we designed a multi-joint robot and NMES hybrid system for the coordinated upper limb physical practice at the elbow, wrist and fingers. Then, the rehabilitation effectiveness with the assistance of the device was evaluated by a pilot single-group trial. EMG signals from target muscles were used for voluntary intention control for both the robot and NMES parts.
The NMES-robot system
Continue —> A Neuromuscular Electrical Stimulation (NMES) and robot hybrid system for multi-joint coordinated upper limb rehabilitation after stroke | Journal of NeuroEngineering and Rehabilitation | Full Text
[Abstract] Assessment of the Ipsilesional Hand Function in Stroke Survivors: The Effect of Lesion Side
The aim of this study was to examine the effect of the side of brain lesion on the ipsilesional hand function of stroke survivors.
Twenty-four chronic stroke survivors, equally allocated in 2 groups according to the side of brain lesion (right or left), and 12 sex- and age-matched healthy controls performed the Jebsen-Taylor Hand Function Test (JTHFT), the Nine-Hole Peg Test (9HPT), the maximum power grip strength (PwGSmax) test, and the maximum pinch grip strength (PnGSmax) test. Only the ipsilesional hand of the stroke survivors and both hands (left and right) of the controls were assessed.
PwGS max and PnGS max were similar among all tested groups. Performances in JTHFT and 9HPT were affected by the brain injury. Individuals with left brain damage showed better performance in 9HPT than individuals with right brain damage, but performance in JTHFT was similar.
Individuals after a brain injury have the capacity to produce maximum strength preserved when using their ipsilesional hand. However, the dexterity of their hands and digits is affected, in particular for stroke individuals with right brain lesion.
The saying goes that “your left hand doesn’t know what your right hand is doing,” but actually, your left hand is paying more attention than you’d think. Researchers at Tel-Aviv University found that when people practiced finger movements with their right hand while watching their left hand on 3D virtual reality headsets, they could use their left hand more efficiently after the exercise. The work, appearing in Cell Reports, provides a new strategy to improve physical therapy for people with limited strength in their hands.
“We are tricking the brain,” says lead author Roy Mukamel, a professor of psychology at Tel Aviv University in Israel. “This entire experiment ended up being a nice demonstration about how to combine software engineering and neuroscience.”
After completing baseline tests to assess the initial motor skills of each hand, 53 participants strapped on virtual reality headsets, which showed simulated versions of their hands. During the first experiment, the participants completed a series of finger movements with their right hand while the screen showed their virtual left hand moving instead. Next, the participants put a motorized glove on their left hand, which moved their fingers to match the motions of the right hand. While this occurred, the headsets again showed their virtual left hand moving instead of their right.
After analyzing the results, the researchers discovered that the left hand’s performance significantly improved (i.e., had more precise movements in a faster amount of time) when the screen showed the left hand. But the most notable improvements occurred when the virtual reality screen showed the left hand moving while the motorized glove moved the right hand in reality.
The researchers also used fMRI to track which brain structures were activated during the experiments in 18 of the participants. The scientists noted that one section of the brain, called the superior parietal lobe, was activated in each person during training. They also discovered that the level of activity in this brain region was correlated to the level of improved performance in the left hand–the more activity, the better the left hand performed.
“Technologically these experiments were a big challenge,” says Mukamel. “We manipulated what people see and combined it with the passive movement of the hand to show that our hands can learn when they’re not moving under voluntary control.”
The researchers are optimistic that this research can be applied to patients in physical therapy programs who have lost the strength or control of their hands. “We need to show a way to obtain high-performance gains relative to other traditional types of therapies,” says Mukamel. “If we can train one hand without voluntarily moving it and still show significant improvements in the motor skills of that hand, then that’s the ideal.”
This work was supported through the Sagol School of Neuroscience and School of Psychological Sciences at Tel-Aviv University in Israel.
Article: Neural Network Underlying Intermanual Skill Transfer in Humans, Ossmy and Mukamel, Cell Reports, 10.1016/j.celrep.2016.11.009, published 13 December 2016.