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.

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.

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.

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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.