This paper presents a holistic and authoritative review of the role of microwave technologies in enabling a new generation of wearable devices. A human-centric Internet of Things (IoT) covering remote healthcare, distributed sensing, and consumer electronics, calls for high-performance wearable devices integrated into clothing, which require interdisciplinary research efforts to emerge. Microwaves, the “interconnect” of wireless networks, can enable, rather than solely connect, the next generation of autonomous, sustainable, and wearable-friendly electronics. First, enabling technologies including wireless power transmission and RF energy harvesting, backscattering and passive communication, RFID, and electromagnetic sensing are reviewed. We then discuss the key integration platforms, covering smart fabrics and electronic textiles, additive manufacturing, printed electronics, natively-flexible and organic RF semiconductors, and fully-integrated CMOS systems, where opportunities for hybrid integration are highlighted. The emerging research trends, from mmWave 6G, RF sensing and imaging, to healthcare applications including neural implants, drug delivery, and safety upon exposure to microwaves are re-visited and discussed, presenting a future roadmap for interdisciplinary research towards sustainable and reliable next-generation wearables.

The ability to remotely assess vital signs using radar systems may potentialize the healthcare systems, since it does not require any direct interaction with the subject. Nonetheless, these systems present inherent challenges that could compromise the accuracy of the vital signs parameters. For instance, there is a lack of resolution on the cardiac signal, since the radar only detects the chest micro-displacement. Low cardiac resolution prevents the exact localization of signal peaks, compromising the heart rate variability (HRV) assessment. In this work, we use a customized bandpass filter (BPF) obtained through adaptive filtering, applied to the electrocardiography (ECG) signal to generate the corresponding synthetic radar signal. This provides an easy way to extract a cardiac radar signal model, free of issues that arise from random body motion (RBM) or from an eventual decrease in signals quality, which are inherent issues of long-term acquisitions. This model was further used to verify if the signal resolution provided by lower carrier radars is suitable to determine the HRV parameters accurately.

The bio-radar system can measure vital signals accurately, by using the Doppler effect principle, which relates the received signal properties to the distance change between the radar antennas and the subject chest-wall. These systems have countless applications, from short range detection to assist in rescue missions, to long-term applications as for the continuous sleeping monitoring. Once the main applications of these systems intend to monitor subjects during long periods of time and under noisy environments, it is impossible to guarantee the patient immobilization, hence its random motion, as well as other clutter sources, will interfere in the acquired signals. Therefore, the signal processing algorithms developed for these applications have been facing several challenges regarding the random motion detection and mitigation. In this paper, an extended review on the already implemented methods is done, considering continuous wave radars. Several sources of random motion are considered, along with different approaches to compensate the distortions caused by them.

Cardiopulmonary activity measured through contactless means is a hot topic within the research community. The Doppler radar is an approach often used to acquire vital signs in real time and to further estimate their rates, in a remote way and without requiring direct contact with subjects. Many solutions have been proposed in the literature, using different transceivers and operation modes. Nonetheless, all different strategies have a common goal: enhance the system efficiency, reduce the manufacturing cost, and minimize the overall size of the system. Antennas are a key component for these systems since they can influence the radar robustness directly. Therefore, antennas must be designed with care, facing several trade-offs to meet all the system requirements. In this sense, it is necessary to define the proper guidelines that need to be followed in the antenna design. In this manuscript, an extensive review on different antenna designs for non-contact vital signals measurements is presented. It is intended to point out and quantify which parameters are crucial for the optimal radar operation, for non-contact vital signs’ acquisition.

Doppler-based radar systems have been seen as a promising tool to assess vital signs, since they are capable to monitor the respiratory and cardiac signal remotely, by measuring the chest-wall displacement. However, due to the spectral overlap of these signals, their proper separation is a challenging task. In this paper, we demonstrate the effectiveness of using Discrete Wavelet Transform in the cardiac signal extraction, by comparing this method with other approaches widely used in literature, namely a standalone Band-Pass Filtering, the Ensembled Empirical Mode Decomposition, the Continuous Wavelet Transform and the Wavelet Packet Decomposition. The comparison metrics were defined taking into consideration the heart rate computation accuracy, and also the peak detection consistency to further evaluate the Heart Rate Variability. The efficiency of those methods is also tested considering real application scenarios, characterized by non-controlled monitoring environment conditions and the ability to equally assess the vital signs of different subjects, regardless their physical stature.