Gih Sung Chung

A Smart Health Monitoring Chair for Nonintrusive Measurement of Biological Signals

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.

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

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 Cohens kappa was 0.62.

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

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.

Validation of heart rate extraction through an iPhone accelerometer

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.

REM sleep estimation only using respiratory dynamics

Polysomnography (PSG) is currently considered the gold standard for assessing sleep quality. However, the numerous sensors that must be attached to the subject can disturb sleep and limit monitoring to within hospitals and sleep clinics. If data could be obtained without such constraints, sleep monitoring would be more convenient and could be extended to ordinary homes. During rapid-eye-movement (REM) sleep, respiration rate and variability are known to be greater than in other sleep stages. Hence, we calculated the average rate and variability of respiration in an epoch (30 s) by applying appropriate smoothing algorithms. Increased and irregular respiratory patterns during REM sleep were extracted using adaptive and linear thresholds. When both parameters simultaneously showed higher values than the thresholds, the epochs were assumed to belong to REM sleep. Thermocouples and piezoelectric-type belts were used to acquire respiratory signals. Thirteen healthy adults and nine obstructive sleep apnea (OSA) patients participated in this study. Kappa statistics showed a substantial agreement (kappa > 0.60) between the standard and respiration-based methods. One-way ANOVA analysis showed no significant difference between the techniques for total REM sleep. This approach can also be applied to the non-intrusive measurement of respiration signals, making it possible to automatically detect REM sleep without disturbing the subject.

Photoplethysmogram Measurement Without Direct Skin-to-Sensor Contact Using an Adaptive Light Source Intensity Control

We developed a chair-attached, nonintrusive photoplethysmogram (PPG) measuring system for everyday life, unconstrained monitoring using nonskin-contacting sensor-amplifier circuits capable of emitting suitable light intensity adaptable to clothing characteristics. Comparison between proposed and conventional systems showed reasonable correlation and root-mean-squared error levels, indicating its feasibility for unconstrained PPG monitoring.

Ubiquitous House and Unconstrained Monitoring Devices for Home Healthcare System

This paper suggests a healthcare monitoring system using unconstrained measurement devices with ubiquitous techniques. These measurement devices are developed into built-in type and sensor type. The first devices are built-in the households (bed, sofa and toilet seat) and measure the patients heart and respiration activity and estimate a blood pressure. The second devices are placed in a kitchen, front door and every room and detect the patients movements and activities. All digitized raw signals are transmitted to a main PC using Bluetooth wireless network from all ubiquitous devices and sent to hospital laboratory after an analyzing process. This paper suggest ubiquitous healthcare house (u-House) for a practical models of home healthcare system. And it can monitor the patients daily health condition and activity in an unconstrained manner.

REM Sleep Classification with Respiration Rates

Polysomnagraphy, the method evaluating the sleep quality, is the reliable and standard method. However, to perform it, subject has to attach many sensors to their body. It seems intrusive, tangled and interrupting the subjects sleep. For the sake of monitoring the daily sleep pattern, simple method measuring the biosignal nonintrusively is needed. At that point, respiration is a beneficial signal, because it can be easily acquired by check the change of the thorax or abdomen. Respiration is one of the representatives of the reflection of the autonomic nervous system. According to the transition of the sleep stages, autonomic nervous system also changes and it can be shown by respiration. In this study, REM and non-REM sleep is classified by only using respiration for the 3 subjects. To enhance the performance, activity signal acquired by load-cell installed bed is added to divide the wake and REM sleep. At the results, hypnograms with polysomnagraphic data is compared with them. It can be seen that high correlation between the REM sleep and increasing pattern of the respiration rates.

Capacitive driven-right-leg grounding in Indirect-contact ECG measurement

For the reduction of common-mode noise level in Indirect-contact ECG (IDC-ECG) measurement, a driven-right-leg grounding method was applied to the IDC-ECG. Because the IDC-ECG does not require any direct contact between the electrodes and the human skin, it is adequate for un-constraining long-term ECG measurement at home and its various applications are now under development. However, larger 60 Hz noise induced by power line appears in IDC-ECG than in conventional ECG, that is a restriction of IDC-ECG application. In this study, the driven-right-leg ground which has been used in conventional direct-contact ECG, was adapted to the IDC-ECG measurement, by feedback of the inversion of amplified common-mode noise to the body through the conductive textile laid on the chair seat. It was shown that the level of 60Hz power line noise was reduced to about -40 dB when the driven-right-leg gain was 1000.

Heart sounds measurement using PVDF film sensor and their comparison with RR intervals of ECG signals

Heart sounds are generated by the fluctuations of blood velocity and the vibrations of cardiac structure. The auscultation of heart sounds is one of the basic tools for patient diagnosis and provides a lot of information associated with cardiovascular systems. For converting mechanical sound waves into electronic signals, PVDF (polyvinylidene fluoride) film sensor was used as heart sounds measurement device. After suited filtering process, the first (S1) and the second (S2) heart sounds components were plainly distinguished and their peak timings were closely corresponded to reference heart sounds signals measured by microphone. Lastly, there was high correlation between S1 intervals (from PVDF signals) and RR intervals (from ECG signals). So it is expected that S1 intervals can replace RR intervals. In future application, these methods have possibilities for non-intrusive heart rate estimation.