Vital signs such as heart rate and respiration rate are among the most important physiological signals for health monitoring and medical applications. Impulse radio (IR) ultra-wideband (UWB) radar becomes one of the essential sensors in non-contact vital signs detection. The heart pulse wave is easily corrupted by noise and respiration activity since the heartbeat signal has less power compared with the breathing signal and its harmonics. In this paper, a signal processing technique for a UWB radar system was developed to detect the heart rate and respiration rate. There are four main stages of signal processing: (1) clutter removal to reduce the static random noise from the environment; (2) independent component analysis (ICA) to do dimension reduction and remove noise; (3) using low-pass and high-pass filters to eliminate the out of band noise; (4) modified covariance method for spectrum estimation. Furthermore, higher harmonics of heart rate were used to estimate heart rate and minimize respiration interference. The experiments in this article contain different scenarios including bed angle, body position, as well as interference from the visitor near the bed and away from the bed. The results were compared with the ECG sensor and respiration belt. The average mean absolute error (MAE) of heart rate results is 1.32 for the proposed algorithm.

In recent years, there has been significant progress in IoT solutions for a variety of fields. The real-time functionality and remote deployment of IoT solutions are two crucial aspects that are necessary for their successful implementation. To achieve this, external batteries play a major role. While lithium–ion batteries are often the go-to choice for IoT devices, it is essential to recognise that different IoT applications have unique needs. Therefore, it is important to conduct a thorough examination of existing battery solutions and their suitability for various IoT applications. This paper presents an extensive survey of different battery technologies, accompanied by an assessment of their applicability in different IoT applications. The aim is to offer a clear and practical guide for researchers and professionals seeking the best battery solutions for their IoT applications.

Non-contact vital sign monitoring has been an important research topic recently due to the ability to monitor patients for an extended period especially during sleep without requiring uncomfortable attachments. Radar is a popular sensor for vital sign monitoring research. Various algorithms have been proposed for estimating respiration rate and heart rate from the radar data. But many algorithms rely on Fast Fourier Transform (FFT) to convert time domain signal to the frequency domain and estimate vital signs, despite FFT having limitation of frequency resolution being inverse of the time interval of data sample. However, there are other spectral estimation algorithms, which have not been much researched into the suitability of vital sign estimation using radar signals. In this paper, we compared eight different types of spectral estimation algorithms, including FFT, for respiration rate and heart rate estimation of stationary subjects in a controlled environment. The evaluation is based on extensive data consisting of different stationary subject positions. Considering the results, the eligibility of algorithms other than FFT for respiration rate and heart rate estimation is demonstrated. Using this work, researchers can get an overview on which algorithm is suitable for their work without the need to review individual algorithms separately.

Human gestures are widely used as a control interface between the user and the device. Camera and infrared-based sensors offer the state-of-arts solution for human gesture recognition. However, these dominant sensors have raised concerns related to privacy and functioning in different environments. Concurrently, radar sensing technologies are maturing and hold a notable advantage in addressing these issues. Despite this progress, situations may arise where multiple users need to interact simultaneously with the same system, posing challenges for current radar-based solutions. In this paper, we introduce a multi-user gesture recognition system for human computer interaction using a mmWave radar sensor with our own novel points clustering method. Our proposed system can simultaneously track and recognize the gestures for two users, achieving an average accuracy of 92.80% for five gestures. A pilot study is also conducted to evaluate the feasibility of the system.