Notes

Medical, neuroimaging, and also neuropathological portrayal of your patient together with Alzheimer's disease malady because of Pick's pathology.
Unobtrusive monitoring of driver mental states has been regarded as an important element in improving the safety of existing transportation systems. While many solutions exist relying on camera-based systems for e.g., drowsiness detection, these can be sensitive to varying lighting conditions and to driver facial accessories, such as eye/sunglasses. In this work, we evaluate the use of physiological signals derived from sensors embedded directly into the steering wheel. In particular, we are interested in monitoring driver stress levels. To achieve this goal, we first propose a modulation spectral signal representation to reliably extract electrocardiogram (ECG) signals from the steering wheel sensors, thus allowing for heart rate and heart rate variability features to be computed. When input to a simple logistic regression classifier, we show that up to 72% accuracy can be achieved when discriminating between stressful and non-stressful driving conditions. In particular, the proposed modulation spectral signal representation allows for direct quality assessment of the obtained heart rate information, thus can provide additional intelligence to autonomous driver monitoring systems.Running gait assessment for shoe type recommendation to avoid injury often takes place within commercial premises. That is not representative of a natural running environment and may influence normal/usual running characteristics. Typically, assessments are costly and performed by an untrained biomechanist or physiotherapist. Thus, use of a low-cost assessment of running gait to recommend shoe type is warranted. Indeed, the recent impact of COVID has heightened the need for a shift toward remote assessment in general due to social-distancing guidelines and restriction of movement to bespoke assessment facilities. Mymo is a Bluetooth-enabled, inertial measurement unit (IMU) wearable worn on the foot. The wearable transmits inertial data via a smartphone application to the Cloud, where algorithms work to recommend a running shoe based upon the users/runner's pronation and foot-strike location/pattern. Here, an additional algorithm is presented to quantify ground contact time and swing/flight time within the Mymo platform to further inform the assessment of a runner's gait. A large cohort of healthy adult and adolescents (n=203, 91M112F) were recruited to run on a treadmill while wearing the Mymo wearable. Validity of the inertial-based algorithm to quantify ground contact time was established through manual labelling of reference standard ground truth video data, with a presented accuracy between 96.6-98.7% across the two classes with respect to each foot.Clinical Relevance-This establishes the validity of a ground contact and swing times for runner with a low-cost IoT wearable.This paper describes the development of a human electrical phantom in the low-frequency band. Conventional high-hydrous gel phantoms cannot mimic the electrical properties of the human body in the low-frequency band. Titanium oxide coated with antimony-doped tin oxide (ATO/TiO2) was added to the high-hydrous gel phantom, and the electrical properties were evaluated in terms of the amount of material added. The developed phantom had an error of less than 10% in the range of 100 kHz to 1 MHz, which conforms with the electrical properties of human muscles. Particularly, at 125 kHz, the error was 2.71% and 4.35% for relative permittivity and conductivity, respectively. The variation in the electrical properties of the developed phantom was evaluated, and it was confirmed that sufficient reproducibility could be obtained.We have demonstrated a tactile-pattern-integrated sensing window for more consistent photoplethysmogram (PPG) measurements. The pattern is composed of two tiny bumps that measure 500μm in diameter and 300μm in height and allow users to position their finger pulps more consistently on the sensing window over different measurement occasions, simply by following their tactile sensation. We experimentally compared the tactile pattern window to a flat window (without any bumps) for 5 test subjects and found that the sensing window with the tactile pattern significantly helped users obtain more consistent PPG signals than the flat window (p less then 0.01).The use of PPG sensors in mobile phones and wearable watches have been limited to the measurements of heart rates and blood oxygen saturation in spite of widely-spread efforts to expand their applications. This is due to the fluctuations observed between measurements which largely originate from inconsistent placement of fingers on the sensing windows. The integrated tactile pattern could provide consistent and accurate measurements and lead to more successful commercialization of diverse PPG-based mobile healthcare services.Flexible strain sensors with ionic liquids have broad application prospects in various fields such as human-machine interaction, motion monitoring, and soft robots due to their conformability. The manufacture of strain sensors based on ionic liquids mainly relies on traditional molding methods and embedded 3D printing methods. However, these methods are complicated and involve lots of manual operations because of the strong fluidity of ionic liquids. In this paper, we propose the use of high conductivity ionic liquids composed of potassium iodide, glycerin, and polyethylene glycol (KI-Gly-PEG). All-in-one direct ink writing of ionic liquids is possible by adding functional materials into the KI-Gly system to change its rheological property and adjusting temperature during the process to assist in improving printing accuracy. We fabricated a flexible strain sensor with silicone rubber and KI-Gly-PEG solution by the all-in-one direct ink writing method. read more Further, we utilized the strain sensor to monitor the elbow bending angle by analyzing its resistance.Even after successful tumor resection, cancer recurrence remains an important issue for bladder tumors. Intra-operative tissue differentiation can help for diagnostic purposes as well as for ensuring that all cancerous cells are completely removed, therefore, decreasing the risk of recurrence. It has been shown that the electrical properties of tumors differ from healthy tissue due to an altered physiology. This work investigates three sensor configurations to measure the impedance of tissue. Each relies on a four terminal measurement and has a distinct electrode arrangement either inline or as a square. Analytical expressions to calculate the geometry factor of each sensor based on Laplace's equation are derived. The results are verified experimentally and in a finite element simulation. Furthermore, several measurements on pig bladders, both fresh and from frozen storage, are carried out with each sensor.It is shown that the calculated and simulated geometry factors yield the same results and are suitable and uncomplicated methods to determine the geometry factor without an experimental setup. These methods also allow for sensor optimization by knowing the measured potentials before the actual fabrication of the sensor. Moreover, conductivity values close to listed data are obtained for pig bladders, which validates the sensors. Ultimately, the square electrode configuration turns out to be a valid option for minimally invasive sensors, which are necessary for the envisaged application of transurethral bladder cancer diagnostics and surgery. This arrangement both assures reliable data and allows for easier miniaturization than the inline electrode placement.The increasing complexity and memory requirements of neural networks have been slowing down the adoption of AI in low-power wearable devices, which impose important restrictions in computational power and memory footprint. These low-power systems are the key to obtain 24/7 monitoring systems necessary for the current personalized healthcare trend since they do not require constant charging. In this work, we apply Knowledge Distillation to our previously published convolutional-recurrent neural network for cardiac arrhythmia detection and classification. We show that the resulting network halves the memory footprint (138 K parameters) and the number of operations (1.84 MOp) compared to the baseline. By using Knowledge Distillation, this network also achieves significantly higher accuracy after quantization (increase in overall F1 score from 0.779 to 0.828) and is capable of running into a nRF52832 System-on-Chip from Nordic Semiconductors. This promising result lays the groundwork for deployment on resource-constrained embedded platforms such as micro-controllers of the ARM Cortex-M family, thus potentially enabling continuous detection of cardiac arrhythmias in low-power wearable devices.This paper presents a multifunctional sensor interface system-on-chip (SoC) for developing self-powered Electrocardiography (ECG) and Photoplethysmography (PPG) sensing wearable devices. The proposed SoC design consists of switch-capacitor-based LED driver and analog front-end (AFE) for PPG sensing, ECG sensing AFE, and power management unit for energy harvesting from Thermoelectric Generator (TEG), all integrated on a 2×2.5 mm2 chip fabricated in 0.18μm standard CMOS process. We have performed post-layout simulation to verify the functionality and performance of the SoC. The LED driver employs the switch-capacitor-based architecture, which charges a storage capacitor up to 2.1 V and discharges accumulated charge to pass instantaneous current up to 40 mA through a selected LED. The PPG AFE converts the resulting photodiode (PD) current to voltage output with adjustable gain of 114-120 dBΩ and input-referred noise of 119 pARMS within 0.4 Hz-10 kHz. The ECG AFE provides adjustable mid-band gain of 47-63 dB, low-cut frequency of 1.5-6.3 Hz, and input-referred noise of 7.83 µVRMS within 1.5 Hz- 1.2 kHz to amplify/filter the recorded ECG signals. The power management unit is able to perform sufficient energy harvesting with the TEG output voltage as low as 350 mV.In this work, we present a case study to evaluate the connections between sleep, training load, and the perceptions of physical/emotional state of a collegiate, division 1 Women's basketball team. The study took place during the off- (3 weeks) and pre-season (6 weeks) while sleep was tracked using WHOOP wearable straps. Training load was recorded by the strength coach and athletes. Short Recovery and Short Stress (SRSS) questionnaire was used to evaluate the perceptions of athletes on their own emotional and physical states. Our results showed that heart rate measurements are associated with stress levels and recovery perception. We also discovered that the training load was not linked to the sleep variables without the considerations of athletic performance. However, training load may alter perceived stress and recovery which requires further exploration.Wearable hip-protection airbags can effectively protect hip joints when elderly people fall. This has been studied all over the world, but similar products need to use special gas cylinders and replacement of new gas cylinders needs to return to the factory; The team previously designed a mechanical puncture protection system based on standard gas cylinders and standard threaded interfaces, but the airbag still has shortcomings such as the small protective area caused by a single gas cylinder. To solve the above problems, a set of wearable hip automatic protection systems based on micromechanical double gas cylinder rapid puncture (MDGCRP) is now designed. Through a large number of experiments, it was found that the response time of MDGCRP was 92ms and the execution time was 177.5ms. Compared with the single gas cylinder approach, the airbag provides greater protection to the hip while the filling time and module weight remain essentially unchanged. The system is triggered by physical and mechanical methods. Compared with chemical blasting or hot-melt methods, the system has the characteristics of low cost and consumables that can be safely and easily replaced by themselves.
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