Non-invasive remote health monitoring plays a vital role in epidemiological situations such as SARS outbreak (2003), MERS (2015) and the recently ongoing outbreak of COVID-19 because it is extremely risky to get close to the patient due to the spread of contagious infections. Non-invasive monitoring is also extremely necessary in situations where it is difficult to use complicated wired connections, such as ECG monitoring for infants, burn victims or during rescue missions when people are buried during building collapses/earthquakes. Due to the unique characteristics such as higher penetration capabilities, extremely precise ranging, low power requirement, low cost, simple hardware and robustness to multipath interferences, Impulse Radio Ultra Wideband (IR-UWB) technology is appropriate for non-invasive medical applications. IR-UWB sensors detect the macro as well as micro movement inside the human body due to its fine range resolution. The two vital signs, i.e., respiration rate and heart rate, can be measured by IR-UWB radar by measuring the change in the magnitude of signal due to displacement caused by human lungs, heart during respiration and heart beating. This paper reviews recent advances in IR-UWB radar sensor design for healthcare, such as vital signs measurements of a stationary human, vitals of a non-stationary human, vital signs of people in a vehicle, through the wall vitals measurement, neonate's health monitoring, fall detection, sleep monitoring and medical imaging. Although we have covered many topics related to health monitoring using IR-UWB, this paper is mainly focused on signal processing techniques for measurement of vital signs, i.e., respiration and heart rate monitoring.
The diversion of a driver’s attention from driving can be catastrophic. Given that conventional button- and touch-based interfaces may distract the driver, developing novel distraction-free interfaces for the various devices present in cars has becomes necessary. Hand gesture recognition may provide an alternative interface inside cars. Given that cars are the targeted application area, we determined the optimal location for the radar sensor, so that the signal reflected from the driver’s hand during gesturing is unaffected by interference from the motion of the driver’s body or other motions within the car. We implemented a Convolutional Neural Network-based technique to recognize the finger-counting-based hand gestures using an Impulse Radio (IR) radar sensor. The accuracy of the proposed method was sufficiently high for real-world applications.
Digital menu boards (DMB) are convenient for customers as well as sellers. In this paper, we have implemented a DMB using IR-UWB transceivers. Unlike the traditional touch-based interfaces for menu selection, in our proposed system, users can select items from the menu without touching the screen. The screen is used to display the menu, and the users point to the specific menu item to select it. Multiple radar transceivers are used to create a virtual space divided into different grid blocks in front of the digital display. Patterns in the radar data are analyzed using a multiclass support vector machine (SVM) classifier and a histogram of oriented gradient descriptor. The system is trained at two different distances from the radar sensors in order to make it robust against distance changes. The proposed hand pointing-based DMB system was verified through different experiments, with different grid sizes, to investigate accuracy dependence on grid size. The results showed high accuracy; therefore, the system can be used in real-life scenarios.INDEX TERMS Digital menu board (DMB), impulse radio ultrawideband, pattern analysis, gestures recognition.
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