We introduce FAEclust, a novel functional autoencoder framework for cluster analysis of multi-dimensional functional data, data that are random realizations of vector-valued random functions. Our framework features a universal-approximator encoder that captures complex nonlinear interdependencies among component functions, and a universal-approximator decoder capable of accurately reconstructing both Euclidean and manifold-valued functional data. Stability and robustness are enhanced through innovative regularization strategies applied to functional weights and biases. Additionally, we incorporate a clustering loss into the network's training objective, promoting the learning of latent representations that are conducive to effective clustering. A key innovation is our shape-informed clustering objective, ensuring that the clustering results are resistant to phase variations in the functions. We establish the universal approximation property of our non-linear decoder and validate the effectiveness of our model through extensive experiments.

This paper presents the development of self-powered breath monitoring sensors using triboelectric nanogenerators (TENGs) technique fabricated from electro spun polycaprolactone (PCL) and cellulose acetate (CA) fibers. The CA was chemically modified with 0.2M NaOH, achieving a maximum open-circuit voltage (Voc) of 14.3 V, short-circuit current (Isc) of$1.67 \mu \mathrm{A}$and charge (Qsc) of 6.61 nC. Incorporating 3 % chitosan into PCL further enhanced the sensor's performance, yielding a Voc of 20.8 V, Isc of 3.178 µA, and Qsc of 9.9 nC. The sensor demonstrated excellent sensitivity, with a linear response and a sensitivity of 2.209 V/kPa across a pressure range of 0.625-12.5 kPa. It also exhibited rapid response times of 34–36 ms and recovery times of 24–26 ms. The optimized materials and sensor configuration show promise for continuous real-time breath monitoring. This innovation provides a significant advancement in wearable health monitoring systems, enabling early detection of respiratory illnesses and chronic disease management.

A series of experiments was conducted in order to determine the reaction kinetics of various types of heart rate monitoring electrodes. In addition, nonmotion on-body measurements were performed in order to gauge how the difference in reaction kinetics translates to the electrocardiogram signal. Standard solid-gel Ag/AgCl single use monitoring electrodes are used here as the gold standard to which textile electrodes can be compared. The test method created here will serve as a basis to evaluate future heart rate monitoring electrodes.

Sleep apnea is a common sleep disorder in which patient sleep patterns are disrupted due to recurrent pauses in breathing or by instances of abnormally low breathing. Current gold standard tests for the detection of apnea events are costly and have the addition of long waiting times. This paper investigates the use of cheap and easy to use sensors for the identification of sleep apnea events. Combinations of respiration, electrocardiography (ECG) and acceleration signals were analysed. Results show that using features, formed using the discrete wavelet transform (DWT), from the ECG and acceleration signals provided the highest classification accuracy, with an F1score of 0.914. However, the novel employment of just the accelerometer signal during classification provided a comparable F1score of 0.879. By employing one or a combination of the analysed sensors a preliminary test for sleep apnea, prior to the requirement for gold standard testing, can be performed.

This paper presents the fabrication, characterization and the performance of a wearable, robust, flexible and disposable barcode system based on novel ionic liquid polymer gels (ionogels) for monitoring in real time mode the pH of the sweat generated during an exercise period. Up to now sweat analysis has been carried out using awkward methods of collecting sweat followed by laboratory analysis. The approach presented here can provide immediate feedback regarding sweat composition. The great advantage of sweat analysis is the fact that it is a completely non-invasive means of analyzing the wearer's physiological state and ensuring their health and well-being.

Breathing exercises form an essential part of the treatment for respiratory illnesses such as cystic fibrosis. Ideally these exercises should be performed on a daily basis. This paper presents an interactive system using a wearable textile sensor to monitor breathing patterns. A graphical user interface provides visual real-time feedback to patients. The aim of the system is to encourage the correct performance of prescribed breathing exercises by monitoring the rate and the depth of breathing. The system is straight forward to use, low-cost and can be installed easily within a clinical setting or in the home. Monitoring the user with a wearable sensor gives real-time feedback to the user as they perform the exercise, allowing them to perform the exercises independently. There is also potential for remote monitoring where the user's overall performance over time can be assessed by a clinician.

Textile-based sensors offer an unobtrusive method of continually monitoring physiological parameters during daily activities. Chemical analysis of body fluids, noninvasively, is a novel and exciting area of personalized wearable healthcare systems. BIOTEX was an EU-funded project that aimed to develop textile sensors to measure physiological parameters and the chemical composition of body fluids, with a particular interest in sweat. A wearable sensing system has been developed that integrates a textile-based fluid handling system for sample collection and transport with a number of sensors including sodium, conductivity, and pH sensors. Sensors for sweat rate, ECG, respiration, and blood oxygenation were also developed. For the first time, it has been possible to monitor a number of physiological parameters together with sweat composition in real time. This has been carried out via a network of wearable sensors distributed around the body of a subject user. This has huge implications for the field of sports and human performance and opens a whole new field of research in the clinical setting.

A distributed Monte Carlo simulation which models the propagation of light through tissue has been developed. It allows for improved calibration of medical imaging devices for investigating tissue oxygenation in the white matter of the cerebral cortex. The application can distribute the simulation over an unbounded number of processors in parallel. We have found that this application is highly parallelisable resulting in up to 91% efficiency at 60 processors running on a homogeneous Java distributed system. A distributed system with 150 heterogeneous processors was used to simulate the paths of photons in a brain tissue model. We found that the source illumination footprint has an effect on the distribution of photons in the head and that lasers do produce a small beam in a highly scattering medium. This application will help researchers to improve the accuracy of their experiments