Feasibility of force myography for the direct control of an assistive robotic hand orthosis in non-impaired individuals.

BACKGROUND: Assistive robotic hand orthoses can support people with sensorimotor hand impairment in many activities of daily living and therefore help to regain independence. However, in order for the users to fully benefit from the functionalities of such devices, a safe and reliable way to detect their movement intention for device control is crucial. Gesture recognition based on force myography measuring volumetric changes in the muscles during contraction has been previously shown to be a viable and easy to implement strategy to control hand prostheses. Whether this approach could be efficiently applied to intuitively control an assistive robotic hand orthosis remains to be investigated. METHODS: In this work, we assessed the feasibility of using force myography measured from the forearm to control a robotic hand orthosis worn on the hand ipsilateral to the measurement site. In ten neurologically-intact participants wearing a robotic hand orthosis, we collected data for four gestures trained in nine arm configurations, i.e., seven static positions and two dynamic movements, corresponding to typical activities of daily living conditions. In an offline analysis, we determined classification accuracies for two binary classifiers (one for opening and one for closing) and further assessed the impact of individual training arm configurations on the overall performance. RESULTS: We achieved an overall classification accuracy of 92.9% (averaged over two binary classifiers, individual accuracies 95.5% and 90.3%, respectively) but found a large variation in performance between participants, ranging from 75.4 up to 100%. Averaged inference times per sample were measured below 0.15 ms. Further, we found that the number of training arm configurations could be reduced from nine to six without notably decreasing classification performance. CONCLUSION: The results of this work support the general feasibility of using force myography as an intuitive intention detection strategy for a robotic hand orthosis. Further, the findings also generated valuable insights into challenges and potential ways to overcome them in view of applying such technologies for assisting people with sensorimotor hand impairment during activities of daily living.

Challenges and Opportunities of Deep Learning for Cough-Based COVID-19 Diagnosis: A Scoping Review.

In the past two years, medical researchers and data scientists worldwide have focused their efforts on containing the pandemic of coronavirus disease 2019 (COVID-19). Deep learning models have been proven to be capable of efficient medical diagnosis and prognosis in cancer, common lung diseases, and COVID-19. On the other hand, artificial neural networks have demonstrated their potential in pattern recognition and classification in various domains, including healthcare. This literature review aims to report the state of research on developing neural network models to diagnose COVID-19 from cough sounds to create a cost-efficient and accessible testing tool in the fight against the pandemic. A total of 35 papers were included in this review following a screening of the 161 outputs of the literature search. We extracted information from articles on data resources, model structures, and evaluation metrics and then explored the scope of experimental studies and methodologies and analyzed their outcomes and limitations. We found that cough is a biomarker, and its associated information can determine an individual's health status. Convolutional neural networks were predominantly used, suggesting they are particularly suitable for feature extraction and classification. The reported accuracy values ranged from 73.1% to 98.5%. Moreover, the dataset sizes ranged from 16 to over 30,000 cough audio samples. Although deep learning is a promising prospect in identifying COVID-19, we identified a gap in the literature on research conducted over large and diversified data sets.

Submillimeter Sized 2D Electrothermal Optical Fiber Scanner.

Optical scanners are used frequently in medical imaging units to examine and diagnose cancers, assist with surgeries, and detect lesions and malignancies. The continuous growth in optics along with the use of optical fibers enables fabrication of imaging devices as small as a few millimeters in diameter. Most forward viewing endoscopic scanners contain an optical fiber acting as cantilever which is vibrated at resonance. In many cases, more than one actuating element is used to vibrate the optical fiber in two directions giving a 2D scan. In this paper, it is proposed to excite the cantilever fiber using a single actuator and scan a 2D region from its vibrating tip. An electrothermal actuator is optimized to provide a bidirectional (horizontal and vertical) displacement to the cantilever fiber placed on it. A periodic current, having a frequency equal to the resonant frequency of cantilever fiber, was passed through the actuator. The continuous expansion and contraction of the actuator enabled the free end of fiber to vibrate in a circle like pattern. A small change in the actuation frequency permitted the scanning of the area inside the circle.

Controlling a motorized orthosis to follow elbow volitional movement: tests with individuals with pathological tremor.

BACKGROUND: There is a need for alternative treatment options for tremor patients who do not respond well to medications or surgery, either due to side effects or poor efficacy, or that are excluded from surgery. The study aims to evaluate feasibility of a voluntary-driven, speed-controlled tremor rejection approach with individuals with pathological tremor. The suppression approach was investigated using a robotic orthosis for suppression of elbow tremor. Importantly, the study emphasizes the performance in relation to the voluntary motion. METHODS: Nine participants with either Essential Tremor (ET) or Parkinson's disease (PD) were recruited and tested off medication. The participants performed computerized pursuit tracking tasks following a sinusoid and a random target, both with and without the suppressive orthosis. The impact of the Tremor Suppression Orthosis (TSO) at the tremor and voluntary frequencies was determined by the relative power change calculated from the Power Spectral Density (PSD). Voluntary motion was, in addition, assessed by position and velocity tracking errors. RESULTS: The suppressive orthosis resulted in a 94.4% mean power reduction of the tremor (p < 0.001) - a substantial improvement over reports in the literature. As for the impact to the voluntary motion, paired difference tests revealed no statistical effect of the TSO on the relative power change (p = 0.346) and velocity tracking error (p = 0.283). A marginal effect was observed for the position tracking error (p = 0.05). The interaction torque with the robotic orthosis was small (0.62 Nm) when compared to the maximum voluntary torque that can be exerted by adult individuals at the elbow joint. CONCLUSIONS: Two key contributions of this work are first, a recently proposed approach is evaluated with individuals with tremor demonstrating high levels of tremor suppression; second, the impact of the approach to the voluntary motion is analyzed comprehensively, showing limited inhibition. This study also seeks to address a gap in studies with individuals with tremor where the impact of engineering solutions on voluntary motion is unreported. This study demonstrates feasibility of the wearable technology as an effective treatment that removes tremor with limited impediment to intentional motion. The goal for such wearable technology is to help individuals with pathological tremor regain independence in activities affected by the tremor condition. Further investigations are needed to validate the technology.

Voluntary-Driven Elbow Orthosis with Speed-Controlled Tremor Suppression.

Robotic technology is gradually becoming commonplace in the medical sector and in the service of patients. Medical conditions that have benefited from significant technological development include stroke, for which rehabilitation with robotic devices is administered, and surgery assisted by robots. Robotic devices have also been proposed for assistance of movement disorders. Pathological tremor, among the most common movement disorders, is one such example. In practice, the dissemination and availability of tremor suppression robotic systems has been limited. Devices in the marketplace tend to either be non-ambulatory or to target specific functions, such as eating and drinking. We have developed a one degree-of-freedom (DOF) elbow orthosis that could be worn by an individual with tremor. A speed-controlled, voluntary-driven suppression approach is implemented with the orthosis. Typically tremor suppression methods estimate the tremor component of the signal and produce a canceling counterpart signal. The suggested approach instead estimates the voluntary component of the motion. A controller then actuates the orthosis based on the voluntary signal, while simultaneously rejecting the tremorous motion. In this work, we tested the suppressive orthosis using a one DOF robotic system that simulates the human arm. The suggested suppression approach does not require a model of the human arm. Moreover, the human input along with the orthosis forearm gravitational forces, of non-linear nature, are considered as part of the disturbance to the suppression system. Therefore, the suppression system can be modeled linearly. Nevertheless, the orthosis forearm gravitational forces can be compensated by the suppression system. The electromechanical design of the orthosis is presented, and data from an essential tremor patient is used as the human input. Velocity tracking results demonstrate an RMS error of 0.31 rad/s, and a power spectral density shows a reduction of the tremor signal by 99.8%, while the intentional component power was reduced by <1%.

Electro-dry-adhesion.

This work presents novel conductive bioinspired dry adhesives with mushroom caps that enable the use of a synergistic combination of electrostatic and van der Waals forces (electro-dry-adhesion). An increase in shear adhesion bond strength of up to 2046% on a wide range of materials is measured when a maximum electrical field of 36.4 V µm(-1) is applied. A suction effect, due to the shape of the dry adhesive fibers, on overall adhesion was not noted for electro-dry-adhesives when testing was performed at both atmospheric and reduced pressure. Utilization of electrostatics to apply a preloading force to dry adhesive fiber arrays allows increased adhesion even after electrostatic force generation has been halted by ensuring the close contact necessary for van der Waals forces to be effective. A comparison is made between self-preloading of the electro-dry-adhesives and the direct application of a normal preloading pressure resulting in nearly the same shear bond strength with an applied voltage of 3.33 kV on the same sample.