Non-Contact Respiratory Measurement Using a Depth Camera for Elderly People.

Measuring respiration at home for cardiac patients, a simple method that can detect the patient's natural respiration, is needed. The purpose of this study was to develop an algorithm for estimating the tidal volume (TV) and respiratory rate (RR) from the depth value of the chest and/or abdomen, which were captured using a depth camera. The data of two different breathing patterns (normal and deep) were acquired from both the depth camera and the spirometer. The experiment was performed under two different clothing conditions (undressed and wearing a T-shirt). Thirty-nine elderly volunteers (male = 14) were enrolled in the experiment. The TV estimation algorithm for each condition was determined by regression analysis using the volume data from the spirometer as the objective variable and the depth motion data from the depth camera as the explanatory variable. The RR estimation was calculated from the peak interval. The mean absolute relative errors of the estimated TV for males were 14.0% under undressed conditions and 10.7% under T-shirt-wearing conditions; meanwhile, the relative errors for females were 14.7% and 15.5%, respectively. The estimation error for the RR was zero out of a total of 206 breaths under undressed conditions and two out of a total of 218 breaths under T-shirt-wearing conditions for males. Concerning females, the error was three out of a total of 329 breaths under undressed conditions and five out of a total of 344 breaths under T-shirt-wearing conditions. The developed algorithm for RR estimation was accurate enough, but the estimated occasionally TV had large errors, especially in deep breathing. The cause of such errors in TV estimation is presumed to be a result of the whole-body motion and inadequate setting of the measurement area.

Development of a sleep apnea event detection method using photoplethysmography.

We studied the possibility of detection of sleep apnea or hypopnea events from photoplethysmography (PPG) wave variation patterns during sleep. In three patients with suspected sleep apnea syndrome, polysomnography (PSG) and the PPG wave were measured simultaneously during sleep. The characteristics of the PPG wave variation patterns in apnea or hypopnea events detected by PSG were investigated. It was found that pulse rate increases and pulse wave amplitude decreases during apnea or hypopnea events, and the respiratory component of heart rate variability has a tendency to decrease before the apnea or hypopnea events. Also, compared to hypopnea, the ratio of the pulse rate is higher, the reduction of the pulse amplitude is more significant, and the decrease of the degree of respiratory variation component in the apnea event is greater. We devised the apnea / hypopnea detection algorithm using these characteristics and evaluated its effectiveness.

Development of the irregular pulse detection method in daily life using wearable photoplethysmographic sensor.

We developed an arrhythmic pulse detection algorithm from photoplethysmography (PPG) measured in daily life using a wearable PPG sensor, in order to provide a simpler device than a Holter electrocardiograph (ECG). However, PPG is very sensitive to artifacts in daily life, e.g. body movement. First, we analyzed the correlation between the ECG and the PPG measured at the same time when the arrhythmic heartbeat occurred in daily life. Using the correlation characteristics, we developed a detection algorithm of the arrhythmic pulse to distinguish the artifacts ascribable to body movement and evaluated its accuracy. The algorithm detects pulse-to-pulse interval (PPI) and pulse amplitude by a beat to distinguish between irregular PPI by arrhythmic pulse and that by the artifact.