Stress management is essential in this modern civilization to maintain one's stress level low and reduce health risks, since stress is one of the primary causes leading to major chronic health disorders. The present study investigates the validity of stress index (SI) metric that objectively quantifies the psychological acute stress using a disposable adhesive biosensor worn on the chest called as HealthPatch®. Eleven healthy volunteers (n=11) were attached with one HealthPatch sensor at left pectoralis major muscle along the cardiac axis to record modified Lead-II ECG. The subjects carried out a standard Trier Social Stress Test (TSST) protocol. During the study, the subjects filled out state anxiety form-Y1 of the State Anxiety Inventory questionnaire (sSTAI); salivary samples were obtained for salivary alpha-amylase (sAA) and salivary cortisol (sC) measurements; and the HealthPatch sensor data were wirelessly acquired. The data analyses revealed that sSTAI scores were significantly increased (P<;0.001) due to TSST compared to the baseline. But, the changes in both sAA and sC measurements were not significant (P=0.281 and P=0.792, respectively). On the other hand, SI metric of HealthPatch showed significant (P<;0.001) increase (~50%) during TSST, and shown to be sensitive to objectively track acute changes in psychological stress. Thus, HealthPatch biosensor can be valuable for continuous monitoring of psychological health and effective management of stress leading to healthy life.

Traditional systems for energy expenditure (EE) assessment are impractical for continuous monitoring in free-living conditions. The study presents the performance of a chest-worn wireless HealthPatch sensor for the continuous estimation of EE rate and total energy expenditure (TEE) based on the heart rate and acceleration signals of upper torso. Volunteers (n=32) were attached with patch sensors at three locations on chest, a portable metabolic analyzer, three commercial devices: BodyMediaFIT, Nike+FuelBand and FitBitForce for comparative analysis. Participants carried out a protocol consisted of resting, mild, moderate and intense level of exercises that lasted for 90 min. Analyses of correlation, performance errors and agreement were carried out for the EE rate and TEE values of the patch sensor compared to the metabolic analyzer. The correlation coefficient and mean absolute error of patch sensor’s EE rate were 0.94+0.04 and 0.67+0.24 (Kcal/min), respectively for the collective three patch locations. The patch sensor offered the most accurate estimates of TEE with least mean absolute percentage error of <15%, least bias (0.8 Kcal) and narrowest 95% limits of agreement (-79 to 81 Kcal) than the other consumer based wearable sensors...

Remote monitoring and wearable technologies could help to effectively manage health, monitor safety andreduce the staggering health care costs. The present studyis designed to investigate the efficacy of disposable wireless HealthPatch sensors for continuous and long-term monitoring in senior subjects over consecutive 50-days in their home setting. Patch sensor is worn on the chest that allows remote and real-time monitoring of vital sign measurements and falls. Patch data and nightly questionnaire responses were obtained in 76 participants (age 59-85 years) over 3603 days using 1333 patches, collectively. The performance of heart rate, respiration rate and skin temperature was assessed on day-1 and day-4 that estimated the mean absolute errors to be <3 beats/min, <3 breaths/min and <1.2 C, respectively compared to their respective reference devices. The vital signs showed no significant differences between start and end of a 3-day wear cycle. False positive rate of fall detection was 0.0027 falls/day. The participants reported the patch wear very/fairly comfortable for 88.2% of days of wear. The wireless patch sensors have demonstrated high performance over its 3-day wear cycle, great compliance, and positive user feedback on wearability and usability. Thus, the patch sensors are very efficient and suitable for remote long-term monitoring at uncontrolled home setting...

Polysomnography (PSG) is the gold standard that manually quantifies the apnea-hypopnea index (AHI) to assess the severity of sleep apnea syndrome (SAS). This study presents an algorithm that automatically estimates the AHI value using a disposable HealthPatch sensor. Volunteers (n=53, AHI: 0.1?-85.8) participated in an overnight PSG study with patch sensors attached to their chest at three specified locations and data were wirelessly acquired. Features were computed for 150- second epochs of patch sensor data using analyses of heart rate variability, respiratory signals, posture and movements. Linear Support Vector Machine classifier was trained to detect the presence/absence of apnea/hypopnea events for each epoch. The number of epochs identified with events was subsequently mapped to AHI values using quadratic regression analysis. The classifier and regression models were optimized to minimize the mean-square error of AHI based on leave-one-out cross-validation. Comparison of predicted and reference AHI values resulted in linear correlation coefficients of 0.87, 0.88 and 0.92 for the three locations, respectively. The predicted AHI values were subsequently used to classify the control-to-mild apnea group (AHI<15) and moderate-to-severe apnea (AHI>=15) with an accuracy (95% confidence intervals) of 89.4% (77.4?-95.4%), 85.0% (70.9?-92.9%), and 82.9% (67.3?-91.9%) for the three locations, respectively. Overnight physiological monitoring using a wireless patch sensor provides an accurate estimate of AHI...