Photoplethysmography and its application in clinical physiological measurement

Ubiquitous blood pressure (BP) monitoring is needed to improve hypertension detection and control and is becoming feasible due to recent technological advances such as in wearable sensing. Pulse transit time (PTT) represents a well-known potential approach for ubiquitous BP monitoring. The goal of this review is to facilitate the achievement of reliable ubiquitous BP monitoring via PTT. We explain the conventional BP measurement methods and their limitations; present models to summarize the theory of the PTT-BP relationship; outline the approach while pinpointing the key challenges; overview the previous work toward putting the theory to practice; make suggestions for best practice and future research; and discuss realistic expectations for the approach.

/pdf/toward-ubiquitous-blood-pressure-monitoring-via-pulse-3274861uea.pdf

Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice.

The aging population, prevalence of chronic diseases, and outbreaks of infectious diseases are some of the major challenges of our present-day society. To address these unmet healthcare needs, especially for the early prediction and treatment of major diseases, health informatics, which deals with the acquisition, transmission, processing, storage, retrieval, and use of health information, has emerged as an active area of interdisciplinary research. In particular, acquisition of health-related information by unobtrusive sensing and wearable technologies is considered as a cornerstone in health informatics. Sensors can be weaved or integrated into clothing, accessories, and the living environment, such that health information can be acquired seamlessly and pervasively in daily living. Sensors can even be designed as stick-on electronic tattoos or directly printed onto human skin to enable long-term health monitoring. This paper aims to provide an overview of four emerging unobtrusive and wearable technologies, which are essential to the realization of pervasive health information acquisition, including: 1) unobtrusive sensing methods, 2) smart textile technology, 3) flexible-stretchable-printable electronics, and 4) sensor fusion, and then to identify some future directions of research.

/pdf/unobtrusive-sensing-and-wearable-devices-for-health-wgdblf1mea.pdf

Unobtrusive Sensing and Wearable Devices for Health Informatics

In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: (1) vehicle-based measures; (2) behavioral measures and (3) physiological measures. A detailed review on these measures will provide insight on the present systems, issues associated with them and the enhancements that need to be done to make a robust system. In this paper, we review these three measures as to the sensors used and discuss the advantages and limitations of each. The various ways through which drowsiness has been experimentally manipulated is also discussed. We conclude that by designing a hybrid drowsiness detection system that combines non-intusive physiological measures with other measures one would accurately determine the drowsiness level of a driver. A number of road accidents might then be avoided if an alert is sent to a driver that is deemed drowsy.

/pdf/detecting-driver-drowsiness-based-on-sensors-a-review-54cryhhdqd.pdf

Detecting driver drowsiness based on sensors: a review.

In-home healthcare services based on the Internet-of-Things (IoT) have great business potential; however, a comprehensive platform is still missing. In this paper, an intelligent home-based platform, the iHome Health-IoT, is proposed and implemented. In particular, the platform involves an open-platform-based intelligent medicine box (iMedBox) with enhanced connectivity and interchangeability for the integration of devices and services; intelligent pharmaceutical packaging (iMedPack) with communication capability enabled by passive radio-frequency identification (RFID) and actuation capability enabled by functional materials; and a flexible and wearable bio-medical sensor device (Bio-Patch) enabled by the state-of-the-art inkjet printing technology and system-on-chip. The proposed platform seamlessly fuses IoT devices (e.g., wearable sensors and intelligent medicine packages) with in-home healthcare services (e.g., telemedicine) for an improved user experience and service efficiency. The feasibility of the implemented iHome Health-IoT platform has been proven in field trials.

/pdf/a-health-iot-platform-based-on-the-integration-of-17ypefb4pq.pdf

A Health-IoT Platform Based on the Integration of Intelligent Packaging, Unobtrusive Bio-Sensor, and Intelligent Medicine Box

We developed nonintrusive methods for simultaneous electrocardiogram, photoplethysmogram, and ballistocardiogram measurements that do not require direct contact between instruments and bare skin. These methods were applied to the design of a diagnostic chair for unconstrained heart rate and blood pressure monitoring purposes. Our methods were operationalized through capacitively coupled electrodes installed in the chair back that include high-input impedance amplifiers, and conductive textiles installed in the seat for capacitive driven-right-leg circuit configuration that is capable of recording electrocardiogram information through clothing. Photoplethysmograms were measured through clothing using seat mounted sensors with specially designed amplifier circuits that vary in light intensity according to clothing type. Ballistocardiograms were recorded using a film type transducer material, polyvinylidenefluoride (PVDF), which was installed beneath the seat cover. By simultaneously measuring signals, beat-to-beat heart rates could be monitored even when electrocardiograms were not recorded due to movement artifacts. Beat-to-beat blood pressure was also monitored using unconstrained measurements of pulse arrival time and other physiological parameters, and our experimental results indicated that the estimated blood pressure tended to coincide with actual blood pressure measurements. This study demonstrates the feasibility of our method and device for biological signal monitoring through clothing for unconstrained long-term daily health monitoring that does not require user awareness and is not limited by physical activity.

A Smart Health Monitoring Chair for Nonintrusive Measurement of Biological Signals

Purpose
With increasing concern for well-being and recent advances in biomedical technology, healthcare is extending beyond the realm of hospitals. Nonintrusive home healthcare monitoring is essential for ensuring long-term use and preservation of an uninterrupted daily life. Herein, we describe recent progress in nonintrusive measurements of ECG, PPG, respiration and blood pressure using sensors installed in everyday household equipment.

Monitoring physiological signals using nonintrusive sensors installed in daily life equipment

Polysomnography (PSG) involves simultaneous and continuous monitoring of relevant normal and abnormal physiological activity during sleep. At present, an electroencephalography-based rule is generally used for classifying sleep stages. However, scoring the PSG record is quite laborious and time consuming. In this paper, movement and cardiac activity were measured unobtrusively by a load-cell-installed bed, and sleep was classified into two stages: slow-wave sleep and non-slow-wave sleep. From the measured cardiac activity, we extracted heartbeat data and calculated heart rate variability parameters: standard deviation of R-R intervals SDNN, low frequency-to-high frequency ratio, alpha of detrended fluctuation analysis and correlation coefficient of R-R interval. The developed system showed a substantial concordance with PSG results when compared using a contingency test. The mean epoch-by-epoch agreement between the proposed method and PSG was 92.5% and Cohen's kappa was 0.62.

https://iopscience.iop.org/article/10.1088/0967-3334/30/11/002/pdf

Slow-wave sleep estimation on a load-cell-installed bed: a non-constrained method.

The rate of increase in the number of aging population in Korea is very rapid among OECD-member countries. And fall accident is one of the most common factors that threaten the health of the elderly. Therefore, it is needed to develop a fall detection system for the elderly. Most fall detection systems use accelerometers attached on the torso. And in various studies, it was verified that these systems have high sensitivity and high specificity. However, the elderly would feel uncomfortable when banding a sensor on the chest every day. Therefore, in this study, we attached an accelerometer on the shoes to detect fall in the elderly. This prototype system would be improved as a smaller, low-power system in the next study. Also, applying energy harvesting device to this shoe system is being developed to reduce the weight of battery.

Fall detection algorithm for the elderly using acceleration sensors on the shoes

Ubiquitous medical technology may provide advanced utility for evaluating the status of the patient beyond the clinical environment. The iPhone provides the capacity to measure the heart rate, as the iPhone consists of a 3-axis accelerometer that is sufficiently sensitive to perceive tiny body movements caused by heart pumping. In this preliminary study, an iPhone was tested and evaluated as the reliable heart rate extractor to use for medical purpose by comparing with reference electrocardiogram. By comparing the extracted heart rate from acquired acceleration data with the extracted one from ECG reference signal, iPhone functioning as the reliable heart rate extractor has demonstrated sufficient accuracy and consistency.

Gih Sung Chung

Papers

A Smart Health Monitoring Chair for Nonintrusive Measurement of Biological Signals

Monitoring physiological signals using nonintrusive sensors installed in daily life equipment

Slow-wave sleep estimation on a load-cell-installed bed: a non-constrained method.

Fall detection algorithm for the elderly using acceleration sensors on the shoes

Validation of heart rate extraction through an iPhone accelerometer