A non-contact method based on multiple signal classification algorithm to reduce the measurement time for accurately heart rate detection

Systematic Literature Review Regarding Heart Rate and Respiratory Rate Measurement by Means of Radar Technology

The use of radar technology for non-contact measurement of vital parameters is increasingly being examined in scientific studies. Based on a systematic literature search in the PubMed, German National Library, Austrian Library Network (Union Catalog), Swiss National Library and Common Library Network databases, the accuracy of heart rate and/or respiratory rate measurements by means of radar technology was analyzed. In 37% of the included studies on the measurement of the respiratory rate and in 48% of those on the measurement of the heart rate, the maximum deviation was 5%. For a tolerated deviation of 10%, the corresponding percentages were 85% and 87%, respectively. However, the quantitative comparability of the results available in the current literature is very limited due to a variety of variables. The elimination of the problem of confounding variables and the continuation of the tendency to focus on the algorithm applied will continue to constitute a central topic of radar-based vital parameter measurement. Promising fields of application of research can be found in particular in areas that require non-contact measurements. This includes infection events, emergency medicine, disaster situations and major catastrophic incidents.

Wearable Pulse Oximeter for Swimming Pool Safety

The purpose of this research was to develop an algorithm for a wearable device that would prevent people from drowning in swimming pools. The device should detect pre-drowning symptoms and alert the rescue staff. The proposed detection method is based on analyzing real-time data collected from a set of sensors, including a pulse oximeter. The pulse oximetry technique is used for measuring the heart rate and oxygen saturation in the subject's blood. It is an optical method; subsequently, the measurements obtained this way are highly sensitive to interference from the subject's motion. To eliminate noise caused by the subject's movement, accelerometer data were used in the system. If the acceleration sensor does not detect movement, a biosensor is activated, and an analysis of selected physiological parameters is performed. Such a setup of the algorithm allows the device to distinguish situations in which the person rests and does not move from situations in which the examined person has lost consciousness and has begun to drown.

Contactless Real-Time Heartbeat Detection via 24 GHz Continuous-Wave Doppler Radar Using Artificial Neural Networks

The measurement of human vital signs is a highly important task in a variety of environments and applications. Most notably, the electrocardiogram (ECG) is a versatile signal that could indicate various physical and psychological conditions, from signs of life to complex mental states. The measurement of the ECG relies on electrodes attached to the skin to acquire the electrical activity of the heart, which imposes certain limitations. Recently, due to the advancement of wireless technology, it has become possible to pick up heart activity in a contactless manner. Among the possible ways to wirelessly obtain information related to heart activity, methods based on mm-wave radars proved to be the most accurate in detecting the small mechanical oscillations of the human chest resulting from heartbeats. In this paper, we presented a method based on a continuous-wave Doppler radar coupled with an artificial neural network (ANN) to detect heartbeats as individual events. To keep the method computationally simple, the ANN took the raw radar signal as input, while the output was minimally processed, ensuring low latency operation (<1 s). The performance of the proposed method was evaluated with respect to an ECG reference ("ground truth") in an experiment involving 21 healthy volunteers, who were sitting on a cushioned seat and were refrained from making excessive body movements. The results indicated that the presented approach is viable for the fast detection of individual heartbeats without heavy signal preprocessing.

MUSIC-based Non-contact Heart Rate Estimation with Adaptive Window Size Setting

Continuous HR (Heart Rate) monitoring enables the stress estimation in daily life. A Doppler sensor could be a key device to facilitate the non-contact HR estimation. As one of the Doppler sensor-based HR estimation methods, we have previously proposed a MUSIC (MUltiple SIgnal Classification)-based HR estimation method. MUSIC is the algorithm widely used as a tool to estimate DOA (Direction of Arrival). In our previous method, MUSIC spectrum is calculated in each sliding window, and then HR is estimated by the maximum peak detection over the MUSIC spectrum. However, when HR changes largely within the window, several peaks due to heartbeats appear over the MUSIC spectrum, which might cause the incorrect peak detection. Hence, an adaptive window is required so that only one peak appears. In this paper, we propose a MUSIC-based HR estimation method with an adaptive window size setting. When several peaks due to heartbeats appear over the MUSIC spectrum, our proposed method shortens the time window and re-calculates the MUSIC spectrum, which is repeated until only one peak appears. The experimental results showed that our method outperformed not only our previous one but also the other existing MUSIC-based HR estimation one in terms of the estimation accuracy of the HR, the stress indexes CVI (Cardiac Vagal Index) and CSI (Cardiac Sympathetic Index).

Unobtrusive Vital Sign Monitoring in Automotive Environments-A Review

This review provides an overview of unobtrusive monitoring techniques that could be used to monitor some of the human vital signs (i.e., heart activity, breathing activity, temperature and potentially oxygen saturation) in a car seat. It will be shown that many techniques actually measure mechanical displacement, either on the body surface and/or inside the body. However, there are also techniques like capacitive electrocardiogram or bioimpedance that reflect electrical activity or passive electrical properties or thermal properties (infrared thermography). In addition, photopleythysmographic methods depend on optical properties (like scattering and absorption) of biological tissues and-mainly-blood. As all unobtrusive sensing modalities are always fragile and at risk of being contaminated by disturbances (like motion, rapidly changing environmental conditions, triboelectricity), the scope of the paper includes a survey on redundant sensor arrangements. Finally, this review also provides an overview of automotive demonstrators for vital sign monitoring.

Tracking driver's heart rate by continuous-wave Doppler radar

Developing driving safety system with medical assistance devices for preventing accidents has become a major social issue in recent year. These devices have been developed using electrocardiogram (ECG) and photoplethysmogram (PPG) for measuring the heart rate (HR). However, driver should directly contact with the sensor for monitoring the HR. Recently, non-contact system based on continuous-wave Doppler radar has widely studied for monitoring HR. The periodogram by Fast Fourier Transform (FFT) was used for estimating HR. However, if motion artifacts by movement of driver and vehicle vibration contaminate the radar signal, we cannot find spectral peak of HR using FFT. In this paper, we propose a method using multiple signal classification (MUSIC) for estimating HR. We compared MUSIC algorithms with a commonly used FFT method using real experiment data while driving. The results indicate that our proposed method can estimate HR accurately from received radar Doppler signal with motion artifacts.

Super-resolution spectral estimation in short-time non-contact vital sign measurement

Non-contact techniques for measuring vital signs attract great interest due to the benefits shown in medical monitoring, military application, etc. However, the presence of respiration harmonics caused by nonlinear phase modulation will result in performance degradation. Suffering from smearing and leakage problems, conventional discrete Fourier transform (DFT) based methods cannot distinguish the heartbeat component from closely located respiration harmonics in frequency domain, especially in short-time processing. In this paper, the theory of sparse reconstruction is merged with an extended harmonic model of vital signals, aiming at achieving a super-resolution spectral estimation of vital signals by additionally exploiting the inherent sparse prior information. Both simulated and experimental results show that the proposed algorithm has superior performance to DFT-based methods and the recently applied multiple signal classification algorithm, and the required processing window length has been shortened to 5.12 s.