Future Smart and Connected Communities (SCC) will utilize distributed sensors and embedded computing to seamlessly generate meaningful data that can assist individuals, communities, and society with interlocking physical, social, behavioral, economic, and infrastructural interaction. SCC will require newer technologies for seamless and unobtrusive sensing and computation in natural settings. This work presents a new technology for health monitoring with low-cost body-worn disposable fully passive electronic sensors, along with a scanner, smartphone app, and web-server for a complete smart sensor system framework. The novel wireless resistive analog passive (WRAP) sensors are printed using an inkjet printing (IJP) technique on paper with silver inks (Novacentrix Ag B40, sheet resistance of 21 mΩ/sq) and incorporate a few discrete surface mounted electronic components (overall thickness of <1 mm). These zero-power flexible sensors are powered through a wireless inductive link from a low-power scanner (500 mW during scanning burst of 100 ms) by amplitude modulation at the carrier signal of 13.56 MHz. While development of various WRAP sensors is ongoing, this paper describes development of a WRAP temperature sensor in detail as an illustration. The prototypes were functionally verified at various temperatures with energy consumption of as low as 50 mJ per scan. The data is analyzed with a smartphone app that computes severity (Events-of-Interest, or EoI) using a real-time algorithm. The severity can then be anonymously shared with a custom web-server, and visualized either in temporal or spatial domains. This research aims to reduce ER visits of patients by enabling self-monitoring, thereby improving community health for SSC.

Spirometry test, a measure of the patient's lung function, is the gold standard for diagnosis and monitoring of chronic pulmonary diseases. Spirometry is currently being done in hospital settings by having the patients blow the air out of their lungs forcefully and into the spirometer's tubes under the supervision and constant guidance of clinicians. This test is expensive, cumbersome and not easily applicable to every-day monitoring of these patients. The lung mechanism when performing a cough is very similar to when spirometry test is done. That includes a big inhalation, air compression and forceful exhalation. Therefore, it is reasonable to assume that obstruction of lung airways should have a similar effect on both cough features and spirometry measures. This paper explores the estimation of lung obstruction using cough acoustic features. A total number of 3695 coughs were collected from patients from 4 different conditions and 4 different severity categories along with their lung function measures in a clinical setting using a smartphone's microphone and a hospital-grade spirometry lab. After feature-set optimization and model hyperparameter tuning, the lung obstruction was estimated with MAE (Mean Absolute Error) of 8% for COPD and 9% for asthma populations. In addition to lung obstruction estimation, we were able to classify patients' disease state with 91% accuracy and patients' severity within each disease state with 95% accuracy.Clinical Relevance— This enables effort-independent estimation of lung function spirometry parameters which could potentially lead to passive monitoring of pulmonary patients

Identifying non-polysomnographic predictors of sleep apnea severity is an on-going challenge in the sleep research. In this study, we detect the severity of obstructive sleep apnea (OSA) condition using features derived from a single lead electrocardiogram (ECG) signal. We have explored 17 time domain, frequency domain, and nonlinear Heart Rate Variability (HRV) features to distinguish subjects in the severe and non-severe OSA groups. An optimal set of nine features including Poincare plot based metrics was found to provide significant discriminative information (p-value ≤ 0.05) for classification. On the test dataset of 16 ECG records, a classification accuracy, sensitivity, and specificity of 87.5%, 100%, and 83.33% respectively, have been achieved using Subspace Discriminant Ensemble classifier. Results demonstrate that severe OSA detection using a single lead ECG signal can be more practical yet simpler compared to existing techniques.

Early detection of chronic diseases helps to minimize the disease impact on patient’s health and reduce the economic burden. Continuous monitoring of such diseases helps in the evaluation of rehabilitation program effectiveness as well as in the detection of exacerbation. The use of everyday wearables i.e. chest band, smartwatch and smart band equipped with good quality sensor and light weight machine learning algorithm for the early detection of diseases is very promising and holds tremendous potential as they are widely used. In this study, we have investigated the use of acceleration, electrocardiogram, and respiration sensor data from a chest band for the evaluation of obstructive lung disease severity. Recursive feature elimination technique has been used to identity top 15 features from a set of 62 features including gait characteristics, respiration pattern and heart rate variability. A precision of 0.93, recall of 0.91 and F-1 score of 0.92 have been achieved with a support vector machine for the classification of severe patients from the non-severe patients in a data set of 60 patients. In addition, the selected features showed significant correlation with the percentage of predicted FEV1.Clinical Relevance— The study result indicates that wearable sensor data collected during natural walk can be used in the early evaluation of pulmonary patients thus enabling them to seek medical attention and avoid exacerbation. In addition, it may serve as a complementary tool for pulmonary patient evaluation during a 6-minute walk test.