Hypoxia is the state of insufficient tissue blood oxygenation that causes tissue damage and even organ failure. Accurate and timely methods for detecting hypoxia are critically needed. To address this need, a wearable device for the non-invasive and simultaneous detection of sweat lactate and tissue oxygenation levels is developed. The integrated device consists of a hydrogel for colourimetric sweat lactate sensing and optical electronics. Both tissue oxygenation and the colourimetric changes of the hydrogel were optically read out quantitatively and concurrently over the same skin area. Prototype devices were tested on an optical phantom with artificial and real sweat samples, validating its capability to measure these two indicators independently and without interference. Finally, the device was demonstrated to be capable of real-time “on-body” simultaneous monitoring of sweat lactate spikes and tissue oxygenation drops (StO2), which showed strong correlation during a hypoxia protocol.The novel device can be applied in broad clinical and non-clinical settings including post-operative care and measuring athlete endurance in a cost-effective manner.

Peripheral edema, also known as leg swelling, is observed frequently because of various causes such as sitting or standing for a long time, inflammation, injury or diseases in venous circulation system, lymphatic system, kidney and heart. It is also a side effect of chemotherapy or hormonal therapy. But to our knowledge, there is no wearable optical monitor that can quantify changes in the tissue water content related to edema. We have conducted simulations on the minimal number of light source-detector pairs and the wavelengths of the sources that can measure changes in the water fraction in superficial tissues within a compact form factor wearable optical sensor using continuous wave near-infrared spectroscopy (CW NIRS). The wavelength range we have investigated is from 800 to 1100 nm. We will present the results of simulations under various device configurations.

The global outbreak of novel Coronavirus Disease (COVID-19) in 2019 required a method for detecting and continuously monitoring patients with an infectious respiratory disease. Patients infected with acute respiratory disease show symptoms of shallow, rapid breathing and dyspnea due to hypoxia-hypercapnia.

In this paper, we develop a system for monitoring of patients with infectious respiratory disease in real time using NIRS sensors and classifies breathing patterns using deep learning algorithm.

Despite tendon injury cases being on the rise globally, the lack of suitable non-invasive clinical imaging tools results in a lack of consensus for the best treatment option within the community. Here, we explore for the first time 4D multispectral optoacoustic tomography (MSOT) for non-invasive label-free evaluation of healthy human tendons, including the wrist flexors, ankle, and Achilles tendons. 3D morphology could be revealed in real-time, enabling motion tracking for the evaluation of tendon function dynamics. Furthermore, vasculature groups in and around the Achilles tendon could be visualized, and perfusion dynamics in response to localized exercise characterized. Unique in its ability to offer real-time, volumetric, label-free imaging with molecular contrast, and compounded by the method’s relatively low-cost, MSOT has been shown capable of assessing dynamic tendon morphology and haemodynamic physiology in response to controlled motion and exercise. Future studies including abnormal subjects could validate the method as a viable routine clinical tool for tendinopathy management and healing monitoring.

We tested whether near-infrared time-resolved spectroscopy (NIRTRS) is relevant to quantifying oxygenation in human skeletal muscles and identifying brown adipose tissue. Nine healthy men completed the 8-h constant sitting experiment with or without a lower-pressure elastic garment. Prolonged sitting for 8 h induced muscle deoxygenation in lower limb muscles. Conversely, application of the garment successfully prevented the pathophysiological deterioration associated with prolonged sitting. Then, NIRTRS parameters were compared to those determined by 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) with computed tomography (CT) (n=30). The [total-Hb] under thermoneutral conditions are positively correlated with a cold-induced PET parameter (r =0.73, p < 0.05. We conclude that NIRTRS successfully detected deteriorated muscle oxygenation during prolonged sitting and that NIRTRS-determined [total-Hb] in the supraclavicular region is a useful parameter for evaluating brown adipose tissue characteristics in a simple, rapid, non-invasive manner.

Posture and cognitive abilities may interact, with higher cognitive workloads potentially triggering automatic postural control, leading to a more stable platform. However, the neural and physiological mechanisms behind this relationship remain unexplored. To address this, our study investigates cortical activity during single and dual tasks before and after a brief physical activity session. We will recruit twenty healthy participants aged 18 to 65 years and use functional near-infrared spectroscopy (fNIRS) to measure brain activity. Additionally, physiological signals, including heart rate, respiratory rate, and blood pressure, will be recorded with the Biopac system during tasks. This research aims to shed light on the neural and physiological factors affecting posture-cognitive dual-task performance following acute physical activity, potentially guiding future interventions for optimizing performance across different age groups.

We developed an innovative platform that integrates camera sensors into workout equipment, utilizing deep learning and artificial intelligence techniques to provide real-time feedback on users' workout postures. The platform analyzes recorded video data using OpenPose algorithms to identify the workout being performed and compare it with the correct form. Discrepancies are instantly reported to the user for immediate correction and improvement. The platform also measures supplemental workout information and relays it back to the user via a mobile platform. Our goal is to reduce workout-related injuries and provide effective guidance through AI-powered feedback.

To identify optimal kinematic signals for reliable use as inputs to machine learning-based gait monitoring systems (without extensive data processing), we quantified the level of changes in two kinematic signals around three axes in four locations. Wearing inertial motion unit wearables (IMU), 30 typically developing children (8-18yrs) walked on treadmill & outdoor overground at three different speeds giving a sizable normative dataset. Primary outcome measures were curve-based similarity analysis (specifically, cosine, Euclidean distance, Poincare and a newly defined Bilateral Symmetry Dissimilarity Test, BSDT) between treadmill and outdoor over-ground walking. Similarity analysis showed a distinct previously unreported high/middle/bottom banding pattern and superior-inferior shank acceleration (SI shank Acc) and medial-lateral shank angular velocity (ML shank AV) demonstrated the least variability across the different walking conditions (as measured by the BSDT). As secondary outcomes measure, conventional spatiotemporal gait parameters (parameter-based similarity analysis) were measured and showed varying differences across walking speeds consistent with previous literature.

We have developed an integrated wearable to monitor skin temperature, blood and tissue oxygen saturation levels, respiratory rate, and pulse rate. The wearable was tested during different breathing exercises including breath holding, paced breathing, and hypercapnia. Preliminary data showed a consistent agreement between measurements from the wearable with signals measured from a commercial system. In addition, tissue oxygen saturation level measured at the subjects’ chest corresponds better to a breathing exercise than arterial blood oxygen saturation level measured at a fingertip. This result demonstrates the capability of our wearable device in monitoring physiological signs in patients with infectious respiratory diseases.