In recent years, the declining birthrate and aging population have become serious problems in Japan. To solve these problems, we have developed a system based on edge AI. This system predicts the future heart rate during walking in real time and provides feedback to improve the quality of exercise and extend healthy life expectancy. In this paper, we predicted the heart rate in real time based on the proposed system and provided feedback. Experiments were conducted without and with the predicted heart rate, and a comparison was made to demonstrate the effectiveness of the predicted heart rate.

The subject must be motionless for conventional radar-based non-contact vital signs measurements. Additionally, the measurement range is limited by the design of the radar module itself. Although the accuracy of measurements has been improving, the prospects for their application could have been faster to develop. This paper proposed a novel radar-based adaptive tracking method for measuring the heart rate of the moving monitored person. The radar module is fixed on a circular plate and driven by stepping motors to rotate it. In order to protect the user’s privacy, the method uses radar signal processing to detect the subject’s position to control a stepping motor that adjusts the radar’s measurement range. The results of the fixed-route experiments revealed that when the subject was moving at a speed of 0.5 m/s, the mean values of RMSE for heart rate measurements were all below 2.85 beat per minute (bpm), and when moving at a speed of 1 m/s, they were all below 4.05 bpm. When subjects walked at random routes and speeds, the RMSE of the measurements were all below 6.85 bpm, with a mean value of 4.35 bpm. The average RR interval time of the reconstructed heartbeat signal was highly correlated with the electrocardiography (ECG) data, with a correlation coefficient of 0.9905. In addition, this study not only evaluated the potential effect of arm swing (more normal walking motion) on heart rate measurement but also demonstrated the ability of the proposed method to measure heart rate in a multiple-people scenario.

This paper presents a driver status monitoring (DSM) system with body channel communication (BCC) technology to acquire the driver's physiological condition. Specifically, a conductive thread, the receiving electrode, is sewn to the surface of the seat so that the acquired signal can be continuously detected. As a signal transmission medium, body channel characteristics using the conductive thread electrode were investigated according to the driver's pose and the material of the driver's pants. Based on this, a BCC transceiver was implemented using an analog frequency modulation (FM) scheme to minimize the additional circuitry and system cost. We analyzed the heart rate variability (HRV) from the driver's electrocardiogram (ECG) and displayed the heart rate and Root Mean Square of Successive Differences (RMSSD) values together with the ECG waveform in real-time. A prototype of the DSM system with commercial-off-the-shelf (COTS) technology was implemented and tested. We verified that the proposed approach was robust to the driver's movements, showing the feasibility and validity of the DSM with BCC technology using a conductive thread electrode.

Heart rate measurement for mm-wave FMCW radar based on phase analysis comprises a variety of noise. Furthermore, because the breathing and heart frequencies are so close, the harmonic of the breathing signal interferes with the heart rate, and the band-pass filter cannot solve it. On the other hand, because heart rates vary from person to person, it is difficult to choose the basic function of WT (Wavelet Transform). To solve the aforementioned difficulties, we consider performing time-frequency domain analysis on human skin surface displacement data. The PA-LI (Phase Accumulation-Linear Interpolation) joint ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) approach is proposed in this paper, which effectively enhances the signal's SNR, estimates the heart rate, and reconstructs the heartbeat signal. The experimental findings demonstrate that the proposed method can not only extract heartbeat signals with high SNR from the front direction, but it can also detect heart rate from other directions (e.g., back, left, oblique front, and ceiling).

This study presents a method for improving the heartbeat interval accuracy of photoplethysmographic (PPG) sensors at ultra-low sampling rates. Although sampling rate reduction can extend battery life, it increases the sampling error and degrades the accuracy of the extracted heartbeat interval. To overcome these drawbacks, a sampling-error compensation method is proposed in this study. The sampling error is reduced by using linear interpolation and autocorrelation based on the waveform similarity of heartbeats in PPG. Furthermore, this study introduces two-line approximation and first derivative PPG (FDPPG) to improve the waveform similarity at ultra-low sampling rates. The proposed method was evaluated using measured PPG and reference electrocardiogram (ECG) of seven subjects. The results reveal that the mean absolute error (MAE) of 4.11ms was achieved for the heartbeat intervals at a sampling rate of 10Hz, compared with 1-kHz ECG sampling. The heartbeat interval error was also evaluated based on a heart rate variability (HRV) analysis. Furthermore, the mean absolute percentage error (MAPE) of the low-frequency/high-frequency (LF/HF) components obtained from the 10-Hz PPG is shown to decrease from 38.3% to 3.3%. This error is small enough for practical HRV analysis.

We propose an idea generation support system known as the “GUNGEN-Heartbeat” that uses heartbeat variations for creating high quality ideas during brainstorming. This system shows “An indication of a check list” or “An indication to promote deep breathing” at time beyond a value with variance of heart rates. We also carried out comparison experiments to evaluate the usefulness of the system.

This paper describes a non-contact and noise-tolerant heart rate monitoring system using a 24-GHz microwave Doppler sensor. The microwave Doppler sensor placed at some distance from the user's chest detects the small vibrations of the body surface due to the heartbeats. The objective of this work is to detect the instantaneous heart rate (IHR) using this non-contact system in a car, because the possible application of the proposed system is a driver health monitoring based on heart rate variability analysis. IHR can contribute to preventing heart-triggered disasters and to detect mental stress state. However, the Doppler sensor system is very sensitive and it can be easily contaminated by motion artifacts and road noise especially while driving. To address this problem, time-frequency analysis using the parametric method and template matching method are employed. Measurement results show that the Doppler sensor, which is pasted on the clothing surface, can successfully extract the heart rate through clothes. The proposed method achieves 13.1-ms RMS error in IHR measurements conducted on 11 subjects in a car on an ordinary road.

Real-time monitoring of heart rate (HR) and body temperature (BT) is crucial for the prognosis and the diagnosis of cardiovascular disease and healthcare. Since current monitoring systems are too rigid and bulky, it is not easy to attach them to the human body. Also, their large current consumption limits the working time. In this paper, we develop a wireless sensor patch for HR and BT by integrating sensor chip, wireless communication chip, and electrodes on the flexible boards that is covered with non-toxic, but skin-friendly adhesive patch. Our experimental results reveal that the flexible wireless sensor patch can efficiently detect early diseases by monitoring the HR and BT in real time.

The arrival of the era of the Internet of Things (IoT) has ensured the ubiquity of human-sensing technologies. Cameras have become inexpensive instruments for human sensing and have been increasingly used for this purpose. Because cameras produce large quantities of information, they are powerful tools for sensing; however, because camera images contain information allowing individuals to be personally identified, their use poses risks of personal privacy violations. In addition, because IoT-ready home appliances are connected to the Internet, camera-captured images of individual users may be unintentionally leaked. In developing our human-detection method [33], [34], we proposed techniques for detecting humans from unclear images in which individuals cannot be identified; however, a drawback of this method was its inability to detect moving humans. Thus, to enable tracking of humans even through the images are blurred to protect privacy, we introduce a particle-filter framework and propose a human-tracking method based on motion detection and heart-rate detection. We also show how the use of integral images [32] can accelerate the execution of our algorithms. In performance tests involving unclear images, the proposed method yields results superior to those obtained with the existing mean-shift method or with a face-detection method based on Haar-like features. We confirm the acceleration afforded by the use of integral images and show that the speed of our method is sufficient to enable real-time operation. Moreover, we demonstrate that the proposed method allows successful tracking even in cases where the posture of the individual changes, such as when the person lies down, a situation that arises in real-world usage environments. We discuss the reasons behind the superior behavior of our method in performance tests compared to those of other methods.

In this paper, we propose a low-invasive framework to predict changes in cognitive performance using only heart rate variability (HRV). Although a lot of studies have tried to estimate cognitive performance using multiple vital data or electroencephalogram data, these methods are invasive for users because they force users to attach a lot of sensor units or electrodes to their bodies. To address this problem, we proposed a method to estimate cognitive performance using only HRV, which can be measured with as few as two electrodes. However, this can't prevent loss of worker productivity because the workers' productivity had already decreased even if their current cognitive performance had been estimated as being at a low level. In this paper, we propose a framework to predict changes in cognitive performance in the near future. We obtained three principal contributions in this paper: (1) An experiment with 45 healthy male participants clarified that changes in cognitive performance caused by mental workload can be predicted using only HRV. (2) The proposed framework, which includes a support vector machine and principal component analysis, predicts changes in cognitive performance caused by mental workload with 84.4 % accuracy. (3) Significant differences were found in some HRV features for test participants, depending on whether or not their cognitive performance changes had been predicted accurately. These results lead us to conclude that the framework has the potential to help both workers and managerial personnel predict what their performances will be in the near future. This will make it possible to proactively suggest rest periods or changes in work duties to prevent losses in productivity caused by decreases of cognitive work performance.

The purpose of this study was to examine an efficient interval encoding method with a slow-frame-rate image sensor, and show that the encoding can work to capture heart rates from multiple persons. Visible light communication (VLC) with an image sensor is a powerful method for obtaining data from sensors distributed in the field with their positional information. However, the capturing speed of the camera is usually not fast enough to transfer interval information like the heart rate. To overcome this problem, we have developed an event timing (ET) encoding method. In ET encoding, sensor units detect the occurrence of heart beat event and send their timing through a sequence of flashing lights. The first flash signal provides the rough timing and subsequent signals give the precise timing. Our theoretical analysis shows that in most cases the ET encoding method performs better than simple encoding methods. Heart rate transfer from multiple persons was examined as an example of the method's capabilities. In the experimental setup, the developed system successfully monitored heart rates from several participants.

In the field of education such as elementary and middle schools, teachers want to take care of schoolchildren during physical trainings and after-school club activities. On the other hand, in the field of sports such as professional and national-level sports, physical or technical trainers want to manage the health, physical and physiological conditions of athletes during exercise trainings in the grounds. In this way, it is required to monitor vital signs for persons during exercises, however, there are several technical problems to be solved in its realization. In this paper, we present the importance and necessity of vital monitoring for persons during exercises, and to make it possible periodically, reliably and in real-time, we present the solutions which we have so far worked out and point out remaining technical challenges in terms of vital/physical sensing, wireless transmission and human interface.

This report describes a robust method of instantaneous heart rate (IHR) extraction from noisy electrocardiogram (ECG) signals. Generally, R-waves are extracted from ECG using a threshold to calculate the IHR from the interval of R-waves. However, noise increases the incidence of misdetection and false detection in wearable healthcare systems because the power consumption and electrode distance are limited to reduce the size and weight. To prevent incorrect detection, we propose a short-time autocorrelation (STAC) technique. The proposed method extracts the IHR by determining the search window shift length which maximizes the correlation coefficient between the template window and the search window. It uses the similarity of the QRS complex waveform beat-by-beat. Therefore, it has no threshold calculation process. Furthermore, it is robust against noisy environments. The proposed method was evaluated using MIT-BIH arrhythmia and noise stress test databases. Simulation results show that the proposed method achieves a state-of-the-art success rate of IHR extraction in a noise stress test using a muscle artifact and a motion artifact.

This paper presents a new dimension in e-learning by collecting and analyzing physiological data during real-world e-learning sessions. Two different content materials, namely Interactive (IM) and Non-interactive (N-IM), were utilized to determine the physiological state of e-learners. Electrocardiogram (ECG) and Skin Conductance Level (SCL) were recorded continuously while learners experienced IM and N-IM for about 25 minutes each. Data from 18 students were collected for analysis. As a result significant difference between IM and N-IM was observed in SCL (p <.01) meanwhile there were no significance in other indices such as heart rate and its variability, and skin conductance response (SCR). This study suggests a new path in understanding e-learners' physiological state with regard to different e-learning materials; the results of this study suggest a clear distinction in physiological states in the context of different learning materials.

The heart rate variability (HRV) is a measure based on the time position of the electrocardiogram (ECG) R-waves. There is a discussion whether or not we can obtain the HRV pattern from blood pressure (BP). In this paper, we propose a method for estimating HRV from a BP signal based on a HIF algorithm and carrying out experiments to compare BP as an alternative measurement of ECG to calculate HRV. Based on the hypotheses that ECG and BP have the same harmonic behavior, we model an alternative HRV signal using a nonlinear algorithm, called heart instantaneous frequency (HIF). It tracks the instantaneous frequency through a rough fundamental frequency using power spectral density (PSD). A novelty in this work is to use fundamental frequency instead of wave-peaks as a parameter to estimate and quantify beat-to-beat heart rate variability from BP waveforms. To verify how the estimate HRV signals derived from BP using HIF correlates to the standard gold measures, i.e. HRV derived from ECG, we use a traditional algorithm based on QRS detectors followed by thresholding to localize the R-wave time peak. The results show the following: 1) The spectral error caused by misestimation of time by R-peak detectors is demonstrated by an increase in high-frequency bands followed by the loss of time domain pattern. 2) The HIF was shown to be robust against noise and nuisances. 3) By using statistical methods and nonlinear analysis no difference between HIF derived from BP and HRV derived from ECG was observed.

In this paper, we describe a new ultrasonic oscillosensor and its application in a biological information measurement system. This ultrasonic sensor has a cylindrical tank of 26 mm (diameter)20 mm (height) filled with water and an ultrasonic probe. It detects the vibration of the target object by obtaining echo signals reflected from the water surface. This sensor can noninvasively detect the vibration of a patient by placing it under a bed frame. We propose a recognition system for humans in bed. Using this sensor, we could determine whether or not a patient is in the bed. Moreover, we propose a heart rate monitoring system using this sensor. When our system was tested on four volunteers, we successfully detected a heart rate comparable to that in the case of using an electrocardiograph. Fuzzy logic plays a primary role in the recognition. Consequently, this system can noninvasively determine whether a patient is in the bed as well as their heart rate using a constraint-free and compact device.

Heartbeat interval time series is an example of natural signals with 1/f characteristics. The exponent α of the 1/fα spectrum has some clinical significance. But sometimes the 1/f components is superimposed by some sinusoid components in the signal. To estimate the slope accurately, the 1/f component must be extracted from the signal. The singular spectrum analysis (SSA) method is recruited here to perform the task. Experimental results on data from real patients are satisfactory.

The aim of this study is to evaluate mental workload (MWL) quantitatively by HRV (Heart Rate Variability) measures. The electrocardiography and the respiration curve were recorded in five different epochs (1) during a rest condition and (2) during mental arithmetic tasks (addition). In the experiment, subjects added two numbers. The work levels (figures of the number in the addition) were set to one figure, two figures, three figures and four figures. The work level had effects on the mean percent correct, the number of answers and the mean processing time. The psychological evaluation on mental workload obtained by the method of paired comparison increased with the work level. Among the statistical HRV measures, the number of peak and trough waves could distinguish between the rest and the mental loading. However, mental workload for each work level was not evaluated quantitatively by the measure. The HRV measures were also calculated from the power spectrum estimated by the autoregressive (AR) model identification. The ratio of the low frequency power to the high frequency power increased linearly with the work level. In conclusion, the HRV measures obtained by the AR power spectrum analysis were found to be sensitive to changes of mental workload.