Does Cup-Grip Type Affect Tremor among People with Essential Tremor?

Essential tremor (ET) is a movement disorder that may cause functional disability in daily activities, such as drinking from a cup or drawing. This study aims to characterize effects of varied cup-grip types and measured axes on the actual performance of people with ET and find correlations between cup-grip type and measured axes, and spiral drawing measures. Participants (20 with ET and 18 controls) held a cup of water in a steady position in three grip types and drew a spiral. The cup acceleration was measured by the cup triaxial accelerometer, analyzed in X, Y and Z axes (directions); deviation of the measured acceleration from the desired steady position acceleration was computed. Significant group differences were found for outcome measures in all grip types. Among participants with ET, significantly higher measured values were found in the cup’s horizontal plane (X and Y axes) compared to the vertical direction (Z axis) and for on-the-handle versus around-the-cup grips in the X and Y axes. Significant correlations were found between this grip’s measures and spiral-drawing actual performance measures, indicating the measurement axis and grip type may affect actual performance. These findings may support the future development of assistive devices for tremor suppression and personalized supportive therapy.

Towards Essential Hand Tremor Suppression via Pneumatic Artificial Muscles

Essential tremor (ET) is one of the most common movement disorders and can occur unexpectedly and develop indefinitely to any population unit. According to the recorded statistics of people suffering from ET, the disorder affects 5% of people worldwide, thus creating an ever-increasing need to investigate ways for its suppression and treatment. In this article, we investigate the capability of Pneumatic Artificial Muscles (PAMs) to reduce or even suppress ET leading to the relief of the sufferers. In our work, we designed and constructed two iterations of a glovelike setup and attempted to explore the possibility of suppressing ET on different parts of the hand by exerting force on the index finger and metacarpal region. For both glove iterations, we established an experimental protocol based on the adjustment of a force controller. Finally, we evaluated exhaustively the performance of our setup under multiple motion scenarios with the participation of an ET-diagnosed volunteer.

A Review on Wearable Technologies for Tremor Suppression

Tremor is defined as a rhythmic, involuntary oscillatory movement of a body part. Although everyone exhibits a certain degree of tremor, some pathologies lead to very disabling tremors. These pathological tremors constitute the most prevalent movement disorder, and they imply severe difficulties in performing activities of daily living. Although tremors are currently managed through pharmacotherapy or surgery, these treatments present significant associated drawbacks: drugs often induce side effects and show decreased effectiveness over years of use, while surgery is a hazardous procedure for a very low percentage of eligible patients. In this context, recent research demonstrated the feasibility of managing upper limb tremors through wearable technologies that suppress tremors by modifying limb biomechanics or applying counteracting forces. Furthermore, recent experiments with transcutaneous afferent stimulation showed significant tremor attenuation. In this regard, this article reviews the devices developed following these tremor management paradigms, such as robotic exoskeletons, soft robotic exoskeletons, and transcutaneous neurostimulators. These works are presented, and their effectiveness is discussed. The article also evaluates the different metrics used for the validation of these devices and the lack of a standard validation procedure that allows the comparison among them.

Parkinson sEMG signal prediction and generation with Neural Networks

Parkinson’s Disease (PD) is a neurodegenerative disorder characterized by symptoms like resting and action tremors, which cause severe impairments to the patient’s life. Recently, many assistance techniques have been proposed to minimize the disease’s impact on patients’ life. However, most of these methods depend on data from PD’s surface electromyography (sEMG), which is scarce. In this work, we propose the first methods, based on Neural Networks, for predicting, generating, and transferring the style of patient-specific PD sEMG tremor signals. This dissertation contributes to the area by i) comparing different NN models for predicting PD sEMG signals to anticipate resting tremor patterns ii) proposing the first approach based on Deep Convolutional Generative Adversarial Networks (DCGANs) to generate PD’s sEMG tremor signals; iii) applying Style Transfer (ST) for augmenting PD’s sEMG signals with publicly available datasets of non-PD subjects; iv) proposing metrics for evaluating the PD’s signal characterization in sEMG signals. These new data created by our methods could validate treatment approaches on different movement scenarios, contributing to the development of new techniques for tremor suppression in patients.

Developing an Assisting Device to Reduce the Vibration on the Hands of Elders

In our society, elderly people are one of the most vulnerable classes. At present, aging in the population is becoming a more and more serious issue. This might lead to several diseases related to aging such as Parkinson’s disease. From the viewpoint of assistive techniques, a device for disadvantaged groups should be created to lessen some of the inconveniences in their lives. Therefore, in this paper, a wearable mechanism to suppress axial vibration is proposed for people who suffer from unexpected tremors in their daily lives. Some investigations on Parkinson’s patients were carried out to infer their characteristics. A dynamic model of the gyroscopic system was then analyzed to formulate interactive torques in the working space. The control input was quantified concerning balancing the system state from the kinetic energy and using the feedback linearization technique. The framework of the proposed device was then described via mechanical analysis and prototype design. To validate the effectiveness of our approach, the system’s mathematical dynamics were simulated in a MATLAB environment. In a frequency range of 2–6 Hz, the system response adapted well to axial tremors. Our hardware in the proposed design was tested in different test scenarios such as in non-gyro- and gyro-based tremor suppression for real-world applications. Hand tremors were measured using wearable equipment with various levels of amplitude. From these results, it is clear that our method could have an effectiveness of up to 92.6%, which is considerably better than that in the non-gyro case. Hence, this innovative mechanism is expected to be employed in the fields of medical assistance, health care services, and robotics.