This study aimed to produce a prototype system for non-contact vital sign monitoring of the elderly using microwave radar with the intention of reducing the burdens on monitored individuals and nursing caregivers. In addition, we tested the ability of the proposed prototype system to measure the respiratory and heart rates of the elderly in a nursing home and discussed the systems effectiveness and problems by examining results of real-time monitoring. The prototype system consisted of two 24-GHz microwave radar antennas and an analysis system. The antennas were positioned below a mattress to monitor motion on the body surface for measuring cardiac and respiratory rates from the dorsal side of the subjects (23.3 ± 1.2 years) who would be lying on the mattress. The heart rates determined by the prototype system correlated significantly with those measured by electrocardiography (r = 0.92). Similarly, the respiratory rates determined by the prototype correlated with those obtained from respiration curves (r = 0.94). Next, we investigated the effectiveness of the prototype system with 7 elderly patients (93.3 ± 10.56 years) at a nursing home. The proposed system appears to be a promising tool for monitoring the vital signs of the elderly in a way that alleviates the need to attach electrodes overnight to confirm patient safety.

Full-night polysomnography (PSG) has been recognized as the gold standard test for sleep apnea-hypopnea syndrome (SAHS). However, PSG examinees are physically restrained for the full night by many contact sensors and obtrusive connecting cables, inducing mental stress. We developed a non-contact SAHS diagnostic system that can detect apneic events without inducing stress in monitored individuals. Two Doppler radars were installed beneath the mattress to measure the vibrations of the chest and abdomen, respectively. Our system determines apnea and hypopnea events when the radar output amplitude decreases by <20 and 70 %, respectively, of the amplitude of a normal breath (without SAHS events). Additionally, we proposed a technique that detects paradoxical movements by focusing on phase differences between thoracic and abdominal movements, and were able to identify three types of sleep apnea: obstructive, central, and mixed. Respiratory disturbance indexes obtained showed a higher correlation (r = 94 %) with PSG than with pulse oximetry (r = 89 %). When predicting the severity of SAHS with an apnea-hypopnea index (AHI) of >15/h or >30/h using PSG as a reference, the radar system achieved a sensitivity of 96 and 90 %, and a specificity of 100 and 79 % with an AHI of >15/h and >30/h, respectively. The proposed radar system can be used as an alternative to the current airflow sensor, and to chest and abdomen belts for apnea-hypopnea evaluation.

There were two key problems in applying Doppler radar to a diagnosis system for sleep apnea-hypopnea syndrome. The first is noise associated with body movements and the second is the body positions in bed and the changes of the sleeping posture. We focused on the changes of the amplitude of the radar output signal corresponding to the changes in the tidal volume, and proposed a method of detecting the change of the respiratory amplitude value without the influence of body position in bed. In addition, we challenged the detection of the apnea-hypopnea event confirmed by accompanied rise of heart rates. To increase the accuracy of heart rate measurement, we propose a new automatic gain control and a real-time radar-output channel selection method based on a spectrum shape analysis. A prototype of the system was set up at a sleep disorder center in a hospital and field tests were carried out with eight subjects. Despite the subjects engaging in frequent body movements while sleeping, the system was quite effective in the diagnosis of sleep apnea-hypopnea syndrome (the correlation coefficient r = 0.98).

Disturbed sleep has become more common in recent years. To increase the quality of sleep, undergoing sleep observation has gained interest as an attempt to resolve possible problems. In this paper, we evaluate a non-restrictive and non-contact method for classifying real-time sleep stages and report on its potential applications. The proposed system measures body movements and respiratory signals of a sleeping person using a multiple 24-GHz microwave radar placed beneath the mattress. We determined a body-movement index to identify wake and sleep periods, and fluctuation indices of respiratory intervals to identify sleep stages. For identifying wake and sleep periods, the rate agreement between the body-movement index and the reference result using the R&K method was 83.5 ± 6.3%. One-minute standard deviations, one of the fluctuation indices of respiratory intervals, had a high degree of contribution and showed a significant difference across the three sleep stages (REM, LIGHT, and DEEP; p <; 0.001). Although the degree that the 5-min fractal dimension contributed-another fluctuation index-was not as high as expected, its difference between REM and DEEP sleep was significant (p <; 0.05). We applied a linear discriminant function to classify wake or sleep periods and to estimate the three sleep stages. The accuracy was 79.3% for classification and 71.9% for estimation. This is a novel system for measuring body movements and body-surface movements that are induced by respiration and for measuring high sensitivity pulse waves using multiple radar signals. This method simplifies measurement of sleep stages and may be employed at nursing care facilities or by the general public to increase sleep quality.

We developed a practicable, non-contact, autonomic activation monitoring system using microwave radars without imposing any stress on monitored individuals. Recently, the rapid increase in the aging population has raised concerns in developed countries. Thus, hospitals and care facilities will need to perform long-term health monitoring of elderly patients. The system allows monitoring of geriatric autonomic dysfunctions caused by chronic diseases, such as diabetes or myocardial infarction (MI), while measuring vital signs in non-contact way. The system measures heart rate variability (HRV) of elderly people in bed using dual, 24-GHz, compact microwave radars attached beneath the bed mattress. HRV parameters (LF, HF, and LF/HF) were determined from the cardiac peak-to-peak intervals, which were detected by radars using the maximum entropy method. We tested the system on 15 elderly people with and without diabetes or MI (72-99 years old) from 7:00 p.m. to 6:00 a.m. at a special nursing home in Tokyo. LF/HF obtained by the system correlated significantly (R = 0.89; p < 0.01) with those obtained by Holter electrocardiography (ECG). Diabetic subjects showed significantly lower LF (radar) than non-diabetic (119.8 ± 57.8 for diabetic, 405.9 ± 112.6 for non-diabetic, p < 0.01). HF (radar) of post-MI subjects was significantly lower than that of non-MI (219.7 ± 131.7 for post-MI and 580.0 ± 654.6 for non-MI, p < 0.05). Previous studies using conventional ECG reveal that diabetic neuropathy decreases LF, and also MI causes parasympathetic attenuation which leads to HF reduction. Our study showed that average SDNN of post-MI patients is smaller than 50 ms which is known to have high mortality. The non-contact autonomic activation monitoring system allows a long-term health management especially during sleeping hours for elderly people at healthcare facilities.

We have developed a non-contact heart rate monitoring system for elderly people In bed using two radars placed on the bed base. The system is designed to increase accuracy despite body motion noise and change of body position and sleeping posture In bed. In order to achieve this, we combined an automatic gaIn control (AGC) method with a real-time radar-output channel selection method which is based on a spectrum shape analysis (SSA). Field tests were carried out with elderly subjects at a nursing home. The accuracy was maintained because the system successfully avoided the null detection point (NDP) problem, respiratory harmonic interference and intermodulation problems. The heart rate accuracy (r = 0.703) was higher than that of the conventional method. The system was proved to be effective In monitoring vital signs without the need for any physical contact with the subjects.

The gold standard test for sleep apnea-hypopnea syndrome is full-night polysomnography (PSG). However, PSG with multiple electrodes is extremely stressful for patients. We have developed a non-contact screening system using two Doppler radars. We propose two methods for the diagnosis: the first is a time-varying amplitude baseline method, and the second is a hypopnea detecting technique that focuses on an increase in heart rates. Using radar signals we detected respiratory effort and the paradoxical movements between chest and abdomen, and demonstrated the ability to identify the three types of sleep apnea. Our developed system was evaluated on 31 outpatients at a hospital. The simultaneous comparison of our system with PSG showed that the respiratory disturbance indexes obtained from radars correlated very closely (90% accuracy) with those from PSG.

Disturbed sleep has become more common in recent years. To improve the quality of sleep, undergoing sleep observation has gained interest as a means to resolve possible problems. In this paper, we evaluate a non-restrictive and non-contact method for classifying real-time sleep stages and report on its potential applications. The proposed system measures heart rate (HR), heart rate variability (HRV), body movements, and respiratory signals of a sleeping person using two 24-GHz microwave radars placed beneath the mattress. We introduce a method that dynamically selects the window width of the moving average filter to extract the pulse waves from the radar output signals. The Pearson correlation coefficient between two HR measurements derived from the radars overnight, and the reference polysomnography was the average of 88.3% and the correlation coefficient for HRV parameters was the average of 71.2%. For identifying wake and sleep periods, the body-movement index reached sensitivity of 76.0%, and a specificity of 77.0% with 10 participants. Low-frequency (LF) components of HRV and the LF/HF ratio had a high degree of contribution and differed significantly across the three sleep stages (REM, LIGHT, and DEEP; p <; 0.01). In contrast, high-frequency (HF) components of HRV were not significantly different across the three sleep stages (p > 0.05). We applied a canonical discriminant analysis to identify wake or sleep periods and to classify the three sleep stages with leave-one-out cross validation. Classification accuracy was 66.4% for simply identifying wake and sleep, 57.1% for three stages (wake, REM, and NREM) and 34% for four stages (wake, REM, LIGHT, and DEEP). This is a novel system for measuring HRs, HRV, body movements, and respiratory intervals and for measuring high sensitivity pulse waves using two radar signals. It simplifies measurement of sleep stages and may be employed at nursing care facilities or by the general public to improve sleep quality.