Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics.

This work involves exploring non-invasive sensor technologies for data collection and preprocessing, specifically focusing on novel thermal calibration methods and assessing low-cost infrared radiation sensors for facial temperature analysis. Additionally, it investigates innovative approaches to analyzing acoustic signals for quantifying coughing episodes. The research integrates diverse data capture technologies to analyze them collectively, considering their temporal evolution and physical attributes, aiming to extract statistically significant relationships among various variables for valuable insights. The study delineates two distinct aspects: cough detection employing a microphone and a neural network, and thermal sensors employing a calibration curve to refine their output values, reducing errors within a specified temperature range. Regarding control units, the initial implementation with an ESP32 transitioned to a Raspberry Pi model 3B+ due to neural network integration issues. A comprehensive testing is conducted for both fever and cough detection, ensuring robustness and accuracy in each scenario. The subsequent work involves practical experimentation and interoperability tests, validating the proof of concept for each system component. Furthermore, this work assesses the technical specifications of the prototype developed in the preceding tasks. Real-time testing is performed for each symptom to evaluate the system's effectiveness. This research contributes to the advancement of non-invasive sensor technologies, with implications for healthcare applications such as remote health monitoring and early disease detection.

Sleep Time Estimated by an Actigraphy Watch Correlates With CSF Tau in Cognitively Unimpaired Elders: The Modulatory Role of APOE.

There is increasing evidence of the relationship between sleep and neurodegeneration, but this knowledge is not incorporated into clinical practice yet. We aimed to test whether a basic sleep parameter, as total sleep estimated by actigraphy for 1 week, was a valid predictor of CSF Alzheimer's Disease core biomarkers (amyloid-ß-42 and -40, phosphorylated-tau-181, and total-tau) in elderly individuals, considering possible confounders and effect modifiers, particularly the APOE ε4 allele. One hundred and twenty-seven cognitively unimpaired volunteers enrolled in the Valdecilla Study for Memory and Brain Aging participated in this study. Seventy percent of the participants were women with a mean age of 65.5 years. After adjustment for covariates, reduced sleep time significantly predicted higher t-tau and p-tau. This association was mainly due to the APOE ε4 carriers. Our findings suggest that total sleep time, estimated by an actigraphy watch, is an early biomarker of tau pathology and that APOE modulates this relationship. The main limitation of this study is the limited validation of the actigraphy technology used. Sleep monitoring with wearables may be a useful and inexpensive screening test to detect early neurodegenerative changes.

Early diagnosis of frailty: Technological and non-intrusive devices for clinical detection.

This work analyses different concepts for frailty diagnosis based on affordable standard technology such as smartphones or wearable devices. The goal is to provide ideas that go beyond classical diagnostic tools such as magnetic resonance imaging or tomography, thus changing the paradigm; enabling the detection of frailty without expensive facilities, in an ecological way for both patients and medical staff and even with continuous monitoring. Fried's five-point phenotype model of frailty along with a model based on trials and several classical physical tests were used for device classification. This work provides a starting point for future researchers who will have to try to bridge the gap separating elderly people from technology and medical tests in order to provide feasible, accurate and affordable tools for frailty monitoring for a wide range of users.

Automatic Ankle Angle Detection by Integrated RGB and Depth Camera System.

Depth cameras are developing widely. One of their main virtues is that, based on their data and by applying machine learning algorithms and techniques, it is possible to perform body tracking and make an accurate three-dimensional representation of body movement. Specifically, this paper will use the Kinect v2 device, which incorporates a random forest algorithm for 25 joints detection in the human body. However, although Kinect v2 is a powerful tool, there are circumstances in which the device's design does not allow the extraction of such data or the accuracy of the data is low, as is usually the case with foot position. We propose a method of acquiring this data in circumstances where the Kinect v2 device does not recognize the body when only the lower limbs are visible, improving the ankle angle's precision employing projection lines. Using a region-based convolutional neural network (Mask RCNN) for body recognition, raw data extraction for automatic ankle angle measurement has been achieved. All angles have been evaluated by inertial measurement units (IMUs) as gold standard. For the six tests carried out at different fixed distances between 0.5 and 4 m to the Kinect, we have obtained (mean ± SD) a Pearson's coefficient, r = 0.89 ± 0.04, a Spearman's coefficient, ρ = 0.83 ± 0.09, a root mean square error, RMSE = 10.7 ± 2.6 deg and a mean absolute error, MAE = 7.5 ± 1.8 deg. For the walking test, or variable distance test, we have obtained a Pearson's coefficient, r = 0.74, a Spearman's coefficient, ρ = 0.72, an RMSE = 6.4 deg and an MAE = 4.7 deg.

Liquid level sensor based on dynamic Fabry-Perot interferometers in processed capillary fiber.

In this work, a novel optical fiber sensor capable of measuring both the liquid level and its refractive index is designed, manufactured and demonstrated through simulations and experimentally. For this, a silica capillary hollow-core fiber is used. The fiber, with a sensing length of 1.55 mm, has been processed with a femtosecond laser, so that it incorporates four holes in its structure. In this way, the liquid enters the air core, and it is possible to perform the sensing through the Fabry-Perot cavities that the liquid generates. The detection mode is in reflection. With a resolution of 4 µm (liquid level), it is in the state of the art of this type of sensor. The system is designed so that in the future it will be capable of measuring the level of immiscible liquids, that is, liquids that form stratified layers. It can be useful to determine the presence of impurities in tanks.