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Have Fuentes posted an update 6 months ago
Increased concentrations of lactate levels in blood are often seen in patients with life-threatening cellular hypoperfusion or infections. State-of-the-art techniques used in clinical practice for measuring serum lactate concentrations rely on intermittent blood sampling and do not permit continuous monitoring of this all important parameter in critical care environments.In recent years, Near Infrared (NIR) Spectroscopy has been established as a possible alternative to existing methods that can mitigate these constraints and be used for non-invasive continuous monitoring of lactate. Nevertheless, the dominant absorption of -OH overtone bands of water in the NIR presents a challenge and complicates the accurate detection of other absorbers such as lactate. For this reason, comprehensive analysis of the -OH overtone bands with systematic lactate concentration changes is essential. This paper reports on the analysis of NIR spectra of two aqueous systems of varying concentrations of lactate in saline and whole blood using the principles of Aquaphotomics.The results show distinctive conformational and structural differences in lactate-water binding, which arise due to the molecular interactions of bonds present in respective solvents.We describe the design of a thermometer that can be worn during everyday activities for monitoring core body temperature (CBT) at the skin surface. This sensor estimates the CBT by measuring the heat flux from the body core based on a thermal conductive model. The heat flux is usually affected by the ambient convective conditions (e.g. air conditioner or posture), which in turn affects the model’s accuracy. Thus, we analytically investigated heat conduction and designed a sensor interface that would be robust to convection changes. We performed an in vitro experiment and a preliminary in vivo experiment. The accuracy of CBT in an in vitro experiments was 0.1°C for convective values ranging from 0 to 1.2 m/s. The wearable thermometer has high potential as non-invasive CBT monitor.A new multi-material polymer fiber electrode has been developed for smart clothing applications. MK5108 The conductive fiber is optimized for bipotential measurements such as surface electromyogram (sEMG) and electrocardiogram (ECG). The main benefit of this fiber is its flexibility and being a dry and non-obtrusive electrode. It can be directly integrated into a garment to make a smart textile for real time biopoten-tial monitoring. A customized wireless electronic system has been developed to acquire electrophysiological signal from the fiber. The receiver base station is connected to a PC host running Matlab. The multi-material polymer fiber electrode recording setting were first optimized in length and inter-electrode distance by recording different sEMG signals. The typical sEMG signal to noise ratio ranges from 19.1 dB to 33.9 dB depending on the geometry. These value are comparable with those obtained with Ag/AgCl electrodes and dry electrode-base commercial system such as Delsys Trigno. The frequency domain analysis obtained from the power spectral density reveals that the new flexible fiber-electrode enables high sEMG signals recording quality while being suitable for integration in smart clothing fabric. A muscle fatigue analysis and ECG recording are also presented in this study. The multi-material polymer fiber electrodes demonstrate a viable solution for sEMG and ECG data acquisition.In this report, we introduce a flexible board combining a custom switching circuit and 16 integrated antennas for a time-domain ultrawideband radar for breast health monitoring in one device. The goal of this study is to assess the suitability of the flexible prototype for tumor detection using carbon-polyurethane experimental breast models and comparing the performance to an earlier prototype with a rigid switching circuit and 16 separate antennas. The flexible antenna array allows direct contact with the patient skin while reducing the number of RF and DC cables needed in the previously reported system. We evaluate that the introduced flexible board successfully transmits and receives the microwave signals, and isolates tumor responses using a simple method. However, we observe that the board exhibits an early signal in the recordings of all antenna pairs which corresponds to direct cross-talk on the board and is not part of the signal that has passed through the phantom.Aspiration pneumonia is a life-threatening disease for the elderly. To prevent its risk, regular swallowing assessment is necessary; however, current screening tools for swallow assessment are not widely available and medical experts are insufficient. As a portable assessment tool, we have been developing a smartphone-based realtime monitoring device (GOKURI) which can evaluate swallowing ability based on swallow sounds. For better detection accuracy of the system, we integrated a deep learning model which was developed based on the swallowing anatomy. In this paper, we provide a detailed analysis to see how the swallow sounds detected by the deep learning-based monitor correspond to the actual swallow activities. Also, as an example of practical application of the system, we analyzed the changes of the swallow abilities over time by recording swallow sounds twice for the same participants at a nursing home. To minimize the risk of aspiration pneumonia, caregivers need to understand the disability levels of the patient’s swallows so that safe feeding assistance can be provided. The result of this paper implies the possibility of using GOKURI as a daily swallowing monitor with minimum interventions.Recent work in Automated Dietary Monitoring (ADM) has shown promising results in eating detection by tracking jawbone movements with a proximity sensor mounted on a necklace. A significant challenge with this approach, however, is that motion artifacts introduced by natural body movements cause the necklace to move freely and the sensor to become misaligned. In this paper, we propose a different but related approach we developed a small wireless inertial sensing platform and perform eating detection by mounting the sensor directly on the underside of the jawbone. We implemented a data analysis pipeline to recognize eating episodes from the inertial sensor data, and evaluated our approach in two different conditions in the laboratory and in naturalistic settings. We demonstrated that in the lab (n=9), the system can detect eating with 91.7% precision and 91.3% recall using the leave-one-participant-out cross-validation (LOPO-CV) performance metric. In naturalistic settings, we obtained an average precision of 92.
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