Notes

Disability-Adjusted Life-Years Linked to Intracerebral Hemorrhage along with Supplementary Damage.
The ability to monitor physiological parameters in an individual is paramount for the evaluation of physical health and the detection of health ailments. Wearable technologies are being introduced on a widening scale to address the absence of modular, accessible, and non-invasive health monitoring as compared to medical grade technologies. In this work, an open source wearable garment is presented that is capable of addressing the absence of such a device. The garment is currently capable of recording electrocardiography, photoplethysmography, galvanic skin response, skin temperature, and respiration rate. The garment has a modular and scalable interface to allow for reconfiguration or expansion of sensor modalities at a total component cost of $137. In a small scale study, the garment is able to reveal strong correlation between heart rate and self perceived stress (R = 0.75, p less then .001), showing promise in its ability to capture clinically-relevant physiological information. Based on these results, continued effort will be made to compile a wearable array of sensors tailored to monitor parameters of specific clinical interest.Smart devices are quickly becoming ubiquitous with the rise of portable biosensors and the internet of things. There exists particular interest in enhancing common objects to have smart capabilities and finding inexpensive solutions for diagnostic tools. One such example is transforming paper items into interactive devices and point-of-care analytic products. Temozolomide Due to the lightweight, flexible, and cost-efficient qualities of paper, unobtrusively powering these devices remains an outstanding problem. In this paper, we demonstrate an electrostatic human-touch powered energy harvesting system, integrated with flexible painted conductive electrodes on paper. This system harvests 8.5 nJ of energy and reaches a voltage of 1.3 V on a 10 nF energy storage capacitor. This technology not only provides a method of powering paper-based products with routine human gestures but can also detect human touch for input communication to sensors.A dynamic and low-profile unloader tibiofemoral knee brace is designed and prototyped by synergizing concepts from the fields of microfluidics and soft robotics. Microfluidics provides strategies for miniaturization and multiplexing while soft robotics afford the tools to create soft fluidic actuators and allow compliant and inherently safe robotic assistance as part of clothing. The unloader knee brace provides dynamic response during the gait cycle, where a three-point leverage torque is provided only during the stance phase to contribute to joint stability when required and enhance comfort and compliance.Clinical Relevance- This novel soft robotic brace has the potential to reduce device abandonment due to aesthetics, user non-compliance and discomfort due to a constant three-point leverage torque during the gait cycle. Also, this air microfluidics enabled soft robotic knee brace could be expanded upon to improve the efficacy of braces in general and augment the effects of physical therapy, rehabilitation and treatment of musculoskeletal conditions.Electrical signals produced within the human body can reveal information about a wide variety of physiological processes including physical activity, cardiac health, and psychological state. The industry standard for physiological signal detection is the use of adhesive electrodes that stick onto the skin. These electrodes can irritate the skin over long periods of time and are not reusable, making them a challenge for use in operational environments. Further, these electrodes often require gel to improve signal transduction, leading to changes in signal quality as these gels dry over time. Wearable sensors for operational environments should be comfortable, unobtrusive, and non-stigmatizing while maintaining signal quality high enough to allow the detection of health states. Here, we present the development and test of a set of woven textile electrodes of 8 different sizes for chest-mounted, 3-lead electrocardiogram (ECG) monitoring. Ten male subjects were tested with each of the woven electrode sizes and with one set of adhesive electrodes. A derived performance metric and signal-to-noise ratio were calculated for each set of electrodes for comparison between them. The smallest sized electrodes were found to be least effective, while the 6th of the 8 sizes were found to be most effective.Spirometry test, a measure of the patient's lung function, is the gold standard for diagnosis and monitoring of chronic pulmonary diseases. Spirometry is currently being done in hospital settings by having the patients blow the air out of their lungs forcefully and into the spirometer's tubes under the supervision and constant guidance of clinicians. This test is expensive, cumbersome and not easily applicable to every-day monitoring of these patients. The lung mechanism when performing a cough is very similar to when spirometry test is done. That includes a big inhalation, air compression and forceful exhalation. Therefore, it is reasonable to assume that obstruction of lung airways should have a similar effect on both cough features and spirometry measures. This paper explores the estimation of lung obstruction using cough acoustic features. A total number of 3695 coughs were collected from patients from 4 different conditions and 4 different severity categories along with their lung function measures in a clinical setting using a smartphone's microphone and a hospital-grade spirometry lab. After feature-set optimization and model hyperparameter tuning, the lung obstruction was estimated with MAE (Mean Absolute Error) of 8% for COPD and 9% for asthma populations. In addition to lung obstruction estimation, we were able to classify patients' disease state with 91% accuracy and patients' severity within each disease state with 95% accuracy.Clinical Relevance- This enables effort-independent estimation of lung function spirometry parameters which could potentially lead to passive monitoring of pulmonary patients.Wearable sensors have been investigated for the purpose of gait analysis, namely gait event detection. Many types of algorithms have been developed specifically using inertial sensor data for detecting gait events. Though much attention has turned toward machine learning algorithms, most of these approaches suffer from large computational requirements and are not yet suitable for real-time applications such as in prostheses or for feedback control. Current rules-based algorithms for real-time use often require fusion of multiple sensor signals to achieve high accuracy, thus increasing complexity and decreasing usability of the instrument. We present our results of a novel, rules-based algorithm using a single accelerometer signal from the foot to reliably detect heel-strike and toe-off events. Using the derivative of the raw accelerometer signal and applying an optimizer and windowing approach, high performance was achieved with a sensitivity and specificity of 94.32% and 94.70% respectively, and a timing error of 6.
Homepage: https://www.selleckchem.com/products/Methazolastone.html