Research Library

We developed a medical sensory training device, the device can give a wide variaty of sensations to the skin. During validation tests, we showed that peoples’ capability to distinguish different sensations improved. This may allow us to reduce the pain and restore function in people with neurological diseases.

Stroke is one of the leading causes of disability and patients do not receive sufficient rehabilitation to avoid permanent impairment. Here we present a new rehabilitation platform using serious games controlled with myoelectric pattern recognition which can potentially be made available in the home environment to increase the amount of rehabilitation. Preliminary testing shows promising results, and the platform will be used in a future trial.

Upper limb impairment is common after stroke, and many will not regain full upper limb function. Different technologies based on surface electromyography (sEMG) have been used in stroke rehabilitation, but there is no collated evidence on the different sEMG-driven interventions and their effect on upper limb function in people with stroke. Synthesize existing evidence and perform a meta-analysis on the effect of different types of sEMG-driven interventions on upper limb function in people with stroke. PubMed, SCOPUS, and PEDro databases were systematically searched for eligible randomized clinical trials that utilize sEMG-driven interventions to improve upper limb function assessed by Fugl-Meyer Assessment (FMA-UE) in stroke. The PEDro scale was used to evaluate the methodological quality and the risk of bias of the included studies. In addition, a meta-analysis utilizing a random effect model was performed for studies comparing sEMG interventions to non-sEMG interventions and for studies comparing different sEMG interventions protocols. Twenty-four studies comprising 808 participants were included in this review. The methodological quality was good to fair. The meta-analysis showed no differences in the total effect, assessed by total FMA-UE score, comparing sEMG interventions to nonsEMG interventions (14 studies, 509 participants, SMD 0.14, P 0.37, 95% CI –0.18 to 0.46, I2 55%). Similarly, no difference in the overall effect was found for the meta-analysis comparing different types of sEMG interventions (7 studies, 213 participants, SMD 0.42, P 0.23, 95% CI –0.34 to 1.18, I2 73%). Twenty out of the twenty-four studies, including participants with varying impairment levels at all stages of stroke recovery, reported statistically significant improvements in upper limb function at post-sEMG intervention compared to baseline. This review and meta-analysis could not discern the effect of sEMG in comparison to a non-sEMG intervention or the most effective type of sEMG intervention for improving upper limb function in stroke populations. Current evidence suggests that sEMG is a promising tool to further improve functional recovery, but randomized clinical trials with larger sample sizes are needed to verify whether the effect on upper extremity function of a specific sEMG intervention is superior compared to other non-sEMG or other type of sEMG interventions.

Electrodes used for measuring electric signals from the body are commonly made of metal making them expensive, stiff, non-efficient potentially toxic. We made electrodes made of graphene induced with a laser allows for an economical, soft, and organic electrode. We tested the graphene electrode on the bench and in humans and found that they were more stable in bench testing and rivals metal electrodes in human testing. Graphene electrodes show potential to replace metal electrodes leading to better and cheaper electrodes.

Wireless and leadless millimetre-scale implantable pulse generators, powered and controlled by ultrasonic links, enable the electrical stimulation of neural pathways in anaesthetized rats.

Textile-based electrodes can be used to measure the electrical signals from muscles which can be used for diagnosis, monitoring and treatment. We reviewed articles describing applications of measuring the electrical signals from muscles using textile electrodes and summarised it. Based on the 41 articles we read, we introduce four textile integration levels to describe the various applications.

Controlling a vehicle with signals from the body can give individuals with missing limbs, loss of muscle function, or weak muscles, the opportunity to become independent. In this study, muscle contractions are translated into commands to control a drone. Three different types of control for collision avoidance were tested. All three types performed well, and the drone was able to avoid all obstacles.

EMG-based biofeedback training was administered to the patient with conversion disorder for improving motor impairment of the upper limb without any physical damage. Three months training brought dramatically improvement to muscle activity of her affected forearm. This study shows the efficacy of EMG-based biofeedback training for motor impairment caused by psychological problem.

Myoelectric pattern recognition (MPR) can be used for intuitive control of virtual and robotic effectors in clinical applications such as prosthetic limbs and the treatment of phantom limb pain. The conventional approach is to feed classifiers with descriptive electromyographic (EMG) features that represent the aimed movements. The complexity and consequently classification accuracy of MPR is highly affected by the separability of such features. In this study, classification complexity estimating algorithms were investigated as a potential tool to estimate MPR performance. An early prediction of MPR accuracy could inform the user of faulty data acquisition, as well as suggest the repetition or elimination of detrimental movements in the repository of classes. Two such algorithms, Nearest Neighbor Separability (NNS) and Separability Index (SI), were found to be highly correlated with classification accuracy in three commonly used classifiers for MPR (Linear Discriminant Analysis, Multi-Layer Perceptron, and Support Vector Machine). These Classification Complexity Estimating Algorithms (CCEAs) were implemented in the open source software BioPatRec and are available freely online. This work deepens the understanding of the complexity of MPR for the prediction of motor volition.

To measure electric signals from the muscles electrodes commonly made of metal containing silver are placed on the skin above the muscle, but electrodes made out textiles might be better for long-term use. To ensure that the signal quality is still good we compared textile electrodes with common metal electrodes. We found that the textile electrodes provided as good signals as the metal electrodes, meaning that textile electrodes can be an alternative to metal electrodes