UWB Radar for Non-contact Heart Rate Variability Monitoring and Mental State Classification

Previously, studies reported that falls analysis is possible in the elderly, when using wearable sensors. However, these devices cannot be worn daily, as they need to be removed and recharged from time-to-time due to their energy consumption, data transfer, attachment to the body, etc. This study proposes to introduce a radar sensor, an unobtrusive technology, for risk of falling analysis and combine its performance with an instrumented insole. We evaluated our methods on datasets acquired during a Timed Up and Go (TUG) test where a stride length (SL) was computed by the insole using three approaches. Only the SL from the third approach was not statistically significant (p = 0.2083 > 0.05) compared to the one provided by the radar, revealing the importance of a sensor location on human body. While reducing the number of force sensors (FSR), the risk scores using an insole containing three FSRs and y-axis of acceleration were not significantly different (p > 0.05) compared to the combination of a single radar and two FSRs. We concluded that contactless TUG testing is feasible, and by supplementing the instrumented insole to the radar, more precise information could be available for the professionals to make accurate decision.

Section: Methodsmentioning

confidence: 99%

Risk of Falling in a Timed Up and Go Test Using an UWB Radar and an Instrumented Insole

Ayena

Chioukh

Otis

et al. 2021

“…Moreover, a millimeter-wave FMCW radar was used in [170] to assess the HR variation of 3 different individuals sitting at distances between 0.6 m and 1 m. A reference measurement reading from Apple Watch show a high degree of agreement with the results obtained. Lastly, a UWB radar was used in [171] for classifying the mental states of different individuals into 4 categories, namely, the normal state, the fatigue state, the stress state and the sleep state using their HRV data. The radar operates at a center frequency of 7.29 GHz.…”

Section: Heart Rate Variability Assessment Using Vital Signs Radarmentioning

confidence: 99%

Human Vital Signs Detection Methods and Potential Using Radars: A Review

Kebe

Gadhafi

Mohammad

et al. 2020

135

Continuous monitoring of vital signs, such as respiration and heartbeat, plays a crucial role in early detection and even prediction of conditions that may affect the wellbeing of the patient. Sensing vital signs can be categorized into: contact-based techniques and contactless based techniques. Conventional clinical methods of detecting these vital signs require the use of contact sensors, which may not be practical for long duration monitoring and less convenient for repeatable measurements. On the other hand, wireless vital signs detection using radars has the distinct advantage of not requiring the attachment of electrodes to the subject’s body and hence not constraining the movement of the person and eliminating the possibility of skin irritation. In addition, it removes the need for wires and limitation of access to patients, especially for children and the elderly. This paper presents a thorough review on the traditional methods of monitoring cardio-pulmonary rates as well as the potential of replacing these systems with radar-based techniques. The paper also highlights the challenges that radar-based vital signs monitoring methods need to overcome to gain acceptance in the healthcare field. A proof-of-concept of a radar-based vital sign detection system is presented together with promising measurement results.

“…MPM also has less complexity compared to adaptive continuous wavelet [ 39 ], which needs much tuning and is too sensitive to SNR. Furthermore, since wavelet base techniques still use widowing (variable-sized regions) [ 40 ], they still fail to obtain high resolution.…”

Section: Introductionmentioning

confidence: 99%

Matrix Pencil Method for Vital Sign Detection from Signals Acquired by Microwave Sensors

Chamaani

Akbarpour

Helbig

et al. 2021

Microwave sensors have recently been introduced as high-temporal resolution sensors, which could be used in the contactless monitoring of artery pulsation and breathing. However, accurate and efficient signal processing methods are still required. In this paper, the matrix pencil method (MPM), as an efficient method with good frequency resolution, is applied to back-reflected microwave signals to extract vital signs. It is shown that decomposing of the signal to its damping exponentials fulfilled by MPM gives the opportunity to separate signals, e.g., breathing and heartbeat, with high precision. A publicly online dataset (GUARDIAN), obtained by a continuous wave microwave sensor, is applied to evaluate the performance of MPM. Two methods of bandpass filtering (BPF) and variational mode decomposition (VMD) are also implemented. In addition to the GUARDIAN dataset, these methods are also applied to signals acquired by an ultra-wideband (UWB) sensor. It is concluded that when the vital sign is sufficiently strong and pure, all methods, e.g., MPM, VMD, and BPF, are appropriate for vital sign monitoring. However, in noisy cases, MPM has better performance. Therefore, for non-contact microwave vital sign monitoring, which is usually subject to noisy situations, MPM is a powerful method.