Person identification using deep neural networks on physiological biomarkers during exercise

Much progress has been made in wearable sensors that provide real-time continuous physiological data from non- invasive measurements including heart rate and biofluids such as sweat. This information can potentially be used to identify the health condition of a person by applying machine learning algorithms on the physiological measurements. We present a person identification task that uses machine learning algorithms on a set of biomarkers collected from 30 subjects carrying out a cycling experiment. We compared an SVM and a gated recurrent neural network (RNN) for real-time accuracy using different window sizes of the measured data. Results show that using all biomarkers gave the best results from any of the models. With all biomarkers, the gated RNN model achieved ∼90% accuracy even in a 30 s time window; and ∼92.3% accuracy in a 150 s time window. Excluding any of the biomarkers leads to at least 7.4% absolute accuracy drop for the RNN model. The RNN implementation on the Jetson Nano incurs a low latency of ∼45 ms per inference

Predicting Hydration Status Using Machine Learning Models From Physiological and Sweat Biomarkers During Endurance Exercise: A Single Case Study

Improper hydration routines can reduce athletic performance. Recent studies show that data from noninvasive biomarker recordings can help to evaluate the hydration status of subjects during endurance exercise. These studies are usually carried out on multiple subjects. In this work, we present the first study on predicting hydration status using machine learning models from single-subject experiments, which involve 32 exercise sessions of constant moderate intensity performed with and without fluid intake. During exercise, we measured four noninvasive physiological and sweat biomarkers including heart rate, core temperature, sweat sodium concentration, and whole-body sweat rate. Sweat sodium concentration was measured from six body regions using absorbent patches. We used three machine learning models to determine the percentage of body weight loss as an indicator of dehydration with these biomarkers and compared the prediction accuracy. The results on this single subject show that these models gave similar mean absolute errors, while in general the nonlinear models slightly outperformed the linear model in most of the experiments. The prediction accuracy of using the whole-body sweat rate or heart rate was higher than using core temperature or sweat sodium concentration. In addition, the model trained on the sweat sodium concentration collected from the arms gave slightly better accuracy than from the other five body regions. This exploratory work paves the way for the use of these machine learning models to develop personalized health monitoring together with emerging, noninvasive wearable sensor devices

Real-time smart multisensing wearable platform for monitoring sweat biomarkers during exercise

Sweat secreted by the human eccrine sweat glands can provide valuable biomarker information during exercise in hot and humid conditions. Real-time noninvasive biomarker recordings are therefore useful for evaluating the physiological conditions of an athlete such as their hydration status during endurance exercise. In this work, we describe a platform that in- cludes different sweat biomonitoring prototypes of cost-effective, smart wearable devices for continuous biomonitoring of sweat during exercise. One prototype is based on conformable and disposable soft sensing patches with an integrated multi-sensor array requiring the integration of different sensors and printed sensors with their corresponding functionalization protocols on the same substrate. The second is based on silicon based sensors and paper microfluidics. Both platforms integrate a multi-sensor array for measuring sodium, potassium, and pH in sweat. We show preliminary results obtained from the multi-sensor prototypes placed on two athletes during exercise. We also show that the machine learning algorithms can predict the percentage of body weight loss during exercise from biomarkers such as heart rate and sweat sodium concentration collected over multiple subjects

Multisensing wearables for real-time monitoring of sweat electrolyte biomarkers during exercise and analysis on their correlation with core body temperature

Sweat secreted by the human eccrine sweat glands can provide valuable biomarker information during exercise. Real-time non-invasive biomarker recordings are therefore useful for evaluating the physiological conditions of an athlete such as their hydration status during endurance exercise. This work describes a wearable sweat biomonitoring patch incorporating printed electrochemical sensors into a plastic microfluidic sweat collector and data analysis that shows the real-time recorded sweat biomarkers can be used to predict a physiological biomarker. The system was placed on subjects carrying out an hour-long exercise session and results were compared to a wearable system using potentiometric robust silicon-based sensors and to commercially available HORIBA-LAQUAtwin devices. Both prototypes were applied to the real-time monitoring of sweat during cycling sessions and showed stable readings for around an hour. Analysis of the sweat biomarkers collected from the printed patch prototype shows that their real-time measurements correlate well (correlation coefficient ≥0.65 ) with other physiological biomarkers such as heart rate and regional sweat rate collected in the same session. We show for the first time, that the real-time sweat sodium and potassium concentration biomarker measurements from the printed sensors can be used to predict the core body temperature with root mean square error (RMSE) of 0.02 °C which is 71% lower compared to the use of only the physiological biomarkers. These results show that these wearable patch technologies are promising for real-time portable sweat monitoring analytical platforms, especially for athletes performing endurance exercise

Printed Iontophoretic‐Integrated Wearable Microfluidic Sweat‐Sensing Patch for On‐Demand Point‐Of‐Care Sweat Analysis

In recent years, wearable epidermal sweat sensors have received extensive attention owing to their great potential to provide personalized information on the health status of individuals at the molecular level. For on‐demand medical analysis of sweat in sedentary conditions, a cost‐effective wearable integrated platform combining sweat stimulation, sampling, transport, and analysis is highly desirable. In this work, a printed iontophoretic system integrated into a microfluidic sensing platform, which combines sweat induction, collection, and real‐time analysis of sweat‐ions into a single patch for on‐demand sweat monitoring on human subjects in stationary conditions is reported. The incorporation of microfluidics features facilitates sweat sampling, collection, and guiding through capillary effect. The multisensing sensor array exhibits sensitivity close to Nernstian behavior for sodium, potassium, and pH. The correlation between the concentrations of ions measured with the sweat patch and with ion chromatography analysis demonstrates the applicability of the system for real‐time point‐of‐care monitoring of the health status of individuals. Furthermore, the sweat patch electronic interface with wireless transmission enables real‐time data monitoring and storage over a cloud platform. This printed iontophoretic‐integrated fluidic sweat patch provides a cost‐effective solution for the on‐demand analysis of sweat components for healthcare applications

Analytical assessment of sodium ISFET based sensors for sweat analysis

Body thermoregulation during exercise induces sweating with the consequent loss of water, electrolytes and other compounds. The use of sweat for bioanalytical purposes has recently widespread because it is an easily accessible biofluid that can be noninvasively collected and/or directly monitored using wearable devices. Different sweat biomarkers can be monitored as indicators of the physiological status of an individual. The concentration of sweat Na+ electrolyte provides information about dehydration or hyponatremia events. Among the different sensor technologies applied for Na+ ion detection in sweat, ion-sensitive field-effect transistors (ISFETs) show superior features in terms miniaturization, key for working with the low volumes of sweat available, robustness, scalability and reproducibility. They also offer fast response and low impedance output signal. This work reports an in-depth study that thoroughly assesses the potential of ISFET sensors for sweat Na+ analysis. Results show a reproducible sensitivity of 60.7 ± 0.5 mV (-Log aNa)− 1, high repeatability, and lifetime up to one month. Sensor reliability is demonstrated by analysing 20 sweat samples and results are compared with the ones provided with the standard ion chromatography technique (IC). The statistical analysis demonstrates that Na+ concentrations estimated with the ISFET sensor and IC are in good agreement showing a relative error of up to 20%. Results demonstrate that the sensor presented in this work can potentially be used for the continuous monitoring of Na+ changes in sweat during exercise.ISSN:0925-400

Paediatric and adolescent athletes in Switzerland: age-adapted proposals for pre-participation cardiovascular evaluation.

High-level sports competition is popular among Swiss youth. Even though preparticipation evaluation for competitive athletes is widespread, screening strategies for diseases responsible for sudden death during sport are highly variable. Hence, we sought to develop age-specific preparticipation cardiovascular evaluation (PPCE) proposals for Swiss paediatric and adolescent athletes (under 18 years of age). We recommend that all athletes practising in a squad with a training load of at least 6 hours per week should undergo PPCE based on medical history and physical examination from the age of 12 years on. Prior to 12 years, individual judgement of athletic performance is required. We suggest the inclusion of a standard 12-lead electrocardiogram (ECG) evaluation for all post-pubertal athletes (or older than 15 years) with analysis in accordance with the International Criteria for ECG Interpretation in Athletes. Echocardiography should not be a first-line screening tool but rather serve for the investigation of abnormalities detected by the above strategies. We recommend regular follow-up examinations, even for those having normal history, physical examination and ECG findings. Athletes with an abnormal history (including family history), physical examination and/or ECG should be further investigated and pathological findings discussed with a paediatric cardiologist. Importantly, the recommendations provided in this document are not intended for use among patients with congenital heart disease who require individualised care according to current guidelines