The previously proposed capacitive sheet electrode enables simultaneous acquisition of electrocardiogram (ECG), respiratory movement (RM), and body proximity data. However, its RM detection accuracy remained below 85%. This study aimed to improve the detection accuracy of RM measured from the chest and abdomen during sleep using the sheet electrode placed under the bedsheet. A new detection algorithm was developed by leveraging concurrently measured body proximity signals and identifying periods of body movement. Analysis of overnight measurements from seven subjects revealed the following: (1) the mean detection accuracies of the new algorithm significantly increased$(p<0.01)$by 6.9% for the chest and 8.8% for the abdomen, and (2) the mean accuracies reached 88.8% and 91.2%, respectively. These results demonstrate the superiority of the new algorithm.

In capacitive electrocardiography (cECG) measurements through thick clothing, signal attenuation in the coupling impedance owing to bulky clothes is a major challenge. This paper presents a capacitance multiplier circuit (CMC) to equivalently amplify the small capacitance of coupling to improve the detection performance of the QRS complex in cECG measurement. We acquired cECG signals using the proposed CMC from five male participants wearing$2.87 \text{mm}$-thick clothing. The corresponding results reveal that the mean detection rates of sensitivity, accuracy, and positive predictive value exceed 95% by adjusting the amplification factor. Compared with a conventional measurement system without the CMC, the mean accuracy significantly increases by 57.0%$(p<0.01)$, and notable noise mitigation is achieved.

The recently proposed short-time impedance spectroscopy (short-time IS) measures impedance spectra with high temporal resolution using a nonsinusoidal mode-switching oscillator. With a view to application for muscular disease diagnosis and human-machine interaction, this study presents the temporal and spectral impedance myography using the short-time IS system. We applied the proposed system to the measurement of human forearm impedance and analyzed impedance changes during wrist motion. The forearm's impedance at 40 frequency points was measured by our developed system. The forearm's rapid impedance response during wrist motion was captured with a high temporal resolution of about 0.5 ms, displaying the impedance magnitude and phase angle change as a color map. Furthermore, we experimentally and theoretically demonstrated that difference and differential Cole-Cole plots reduced skin impedance effects and produced a characteristic curve corresponding to wrist motion, even with two-terminal measurement. We anticipate that the IS system and visualization method will be beneficial for myoelectric control in prosthetic hands and noninvasive muscle disease diagnosis.

P-wave indices are suitable predictors of atrial fibrillation. This paper presents the modification of a capacitive electrocardiography measurement system with an electrode sheet and the evaluation of the P-wave detection capability of the modified system. The gain settings of the detection circuit were optimized, leading to high detection rates exceeding 90% in seven of eight males. Furthermore, accuracy and sensitivity were significantly increased by 5.5%$(p < 0.05)$and 4.1%$(p < 0.05)$, respectively, by the use of a new electrode featuring a tilted connecting vector between the centers of gravity of the sensing layers along with the mean P-wave axis.

In this study, we propose a novel Internet of Things (IoT) system with a printed sheet electrode and evaluate its detection ability for bed-exiting behavior. The sheet electrode placed under the bed sheet detects the body proximity of the shoulder and lower back independently through the worn clothes and bed sheet based on capacitive coupling. A detection algorithm implemented in a single-board computer was tested in a bed-exiting experiment with five males wearing restraints to simulate the motions of the elderly. Compared with a commercially available mat sensor, the mean detection time of the proposed system was significantly shorter$(\mathrm{p} < 0.05)$for each subj ect and 3.5 s earlier on average. In addition, there were no false detections among 95 postural changes and 50 bed-exiting behaviors in the experiment.

Daily monitoring of mean arterial pressure (MAP) is required for patients with ventricular assisted device (VAD), who mostly have little pulsatile amplitude in the blood flow. To explore potential indices for estimating MAP, we analyzed photoplethysmogram (PPG) waveform obtained using green (560 nm) wavelength from the earlobe in five healthy subjects. In the setting of resting supine position with 8.5-minutes duration including 0.5-minutes cold test, we found distinct correlation (> 0.7) between DC component of the PPG waveform (DCGr) and MAP in all subjects (mean r=0.765±0.044). Meanwhile, both of pulse arrival time (PATGr) nor pulse amplitude divided by the DC component (mNPVGr) of the green PPG indicated no correlation to MAP (r=-0.136±0.241 and r=-0.150±0.478) in the same setting.

Home monitoring requires measuring the physiological and behavioral signals without impairing a subject's everyday life. This paper presents an integrated and noncontact approach for obtaining simultaneous physiological and behavioral signals of recumbent humans in beds using a home-monitoring application. In the proposed approach, a fabric-sheet unified sensing electrode (FUSE) obtains physiological signals by recording the electrocardiogram (ECG), chest and abdominal respiratory movements (RMs), and ballistocardiogram (BCG). The FUSE also detects the behavioral signals of body proximity (BPx) and lateral/supine lying postures. A prototype system with FUSE was validated in a short-term experiment and 6-h overnight measurements on two different groups composed of seven lying subjects. The results confirmed that the approach senses each signal independently and records the ECG, RMs, BCG, and BPx signals simultaneously. The mean sensitivities of the R and T waves of the ECG during sleep were 86.1% and 88.0%, respectively, whereas those of the chest and abdominal RMs were 90.7% and 90.1%, respectively. Although our prototype system has room for improvement, the results suggest that our approach enables the unconstrained, nocturnal monitoring of the physiological and behavioral signals in recumbent humans. The at-home monitoring of the physiological and behavioral signals is expected to contribute to cost-effective personalized healthcare in the future. This noncontact and easy-to-install system for in-bed measurements can facilitate a new era of home monitoring.