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...

Continuous monitoring of respiratory rate in am- bulatory conditions has widespread applications for screening of respiratory diseases and remote patient monitoring. Unfortunately, minimally obtrusive techniques often suffer from low accuracy. In this paper, we describe an algorithm with low computational complexity for combining multiple respiratory measurements to estimate breathing rate from an unobtrusive chest patch sensor. Respiratory rates derived from the respi- ratory sinus arrhythmia (RSA) and modulation of the QRS amplitude of electrocardiography (ECG) are combined with a respiratory rate derived from tri-axial accelerometer data. The three respiration rates are combined by a weighted average using weights based on quality metrics for each signal. The algorithm was evaluated on 15 elderly subjects who performed spontaneous and metronome breathing as well as a variety of activities of daily living (ADLs). When compared to a reference device, the mean absolute error was 1.02 breaths per minute (BrPM) during metronome breathing, 1.67 BrPM during spontaneous breathing, and 2.03 BrPM during ADLs....

There has been a growing interest in out-of-center sleep testing with portable devices for accurate diagnosis of sleep apnea syndrome. This paper presents a new algorithm that extracts features based on filtering and statistical dispersion of the nasal airflow respiration signal and detects apnea events on a per-second basis. The data records were randomly selected from the Sleep Heart Health Study (SHHS-2) database to represent 100 control subjects with Apnea-Hypopnea Index (AHI) less than 5, and 100 apnea subjects with AHI values from 30 to 75. The algorithm was optimized according to the product of sensitivity and positive predictive value of apnea events among a training dataset of 50 apnea subjects with a constraint on the false positive rate among a training dataset of 50 control subjects. From testing of the algorithm on separate datasets, the false positive rate among 50 control subjects was found to be 1.3 events per hour, which corresponds to 100% specificity of classifying apnea subjects. The sensitivity and positive predictive value among 50 apnea subjects were found to be 83.6% and 72.3%, respectively. Among the identified false positive events in the apnea subjects, 64.3% of the events were found to be hypopnea events. Thus, incorporation of hypopnea detection would enhance the performance of the apnea detection algorithm...