Breathing rate monitoring is a must for hospitalized patients with the current coronavirus disease 2019 (COVID-19). We review in this paper recent implementations of breathing monitoring techniques, where both contact and remote approaches are presented. It is known that with non-contact monitoring, the patient is not tied to an instrument, which improves patients’ comfort and enhances the accuracy of extracted breathing activity, since the distress generated by a contact device is avoided. Remote breathing monitoring allows screening people infected with COVID-19 by detecting abnormal respiratory patterns. However, non-contact methods show some disadvantages such as the higher set-up complexity compared to contact ones. On the other hand, many reported contact methods are mainly implemented using discrete components. While, numerous integrated solutions have been reported for non-contact techniques, such as continuous wave (CW) Doppler radar and ultrawideband (UWB) pulsed radar. These radar chips are discussed and their measured performances are summarized and compared.

Radio frequency energy harvesting (RFEH) is very attractive for the Internet of things (IoT) and self-powered micro-systems such as wearable biomedical devices and wireless sensor networks. This paper proposes, analyzes, and implements a new RF-DC converter in standard 130 nm CMOS technology. The developed converter is designed and optimized for ultra-low-power IoT and wearable biomedical applications using the 900 MHz ISM band. The proposed 10-stage cross-connected rectifier compensates the transistors threshold voltage by using both dynamic and static bias compensation techniques. An analytical model of the rectifier based on the MOSFET transistor equations is presented, allowing optimization of the rectifier as a function of the number of stages and transistors sizing, improving the sensitivity and the input power range of the converter. The measurement results demonstrate a sensitivity of −25.5 dBm for 1 V output across a 5- $\text{M}\Omega $ resistive load and −29 dBm for a 100 $\text{M}\Omega $ load, which is better than the best previously reported results. The measured peak end-to-end efficiency of the proposed harvester is 42.4% at −16 dBm input power, delivering 2.19 V to a 450 $\text{k}\Omega $ load.

Due to the constantly growing geriatric population and the projected increase of the prevalence of chronic diseases that are refractory to drugs, implantable medical devices (IMDs) such as neurostimulators, endoscopic capsules, artificial retinal prostheses, and brain-machine interfaces are being developed. According to many business forecast firms, the IMD market is expected to grow and they are subject to much research aiming to overcome the numerous challenges of their development. One of these challenges consists of designing a wireless power and data transmission system that has high power efficiency, high data rates, low power consumption, and high robustness against noise. This is in addition to minimal design and implementation complexity. This manuscript concerns a comprehensive survey of the latest techniques used to power up and communicate between an external base station and an IMD. Patient safety considerations related to biological, physical, electromagnetic, and electromagnetic interference concerns for wireless IMDs are also explored. The simultaneous powering and data communication techniques using a single inductive link for both power transfer and bidirectional data communication, including the various data modulation/demodulation techniques, are also reviewed. This review will hopefully contribute to the persistent efforts to implement compact reliable IMDs while lowering their cost and upsurging their benefits.

One of the most important topics in the field of wireless sensor networks is the development of approaches to improve network lifetime. In this paper, an energy-efficient control and routing protocol for wireless sensor networks is presented. This algorithm is based on reinforcement learning for energy management in the network. This protocol seeks to optimize routing policies to maximize the long-term reward received by each node, using reinforcement learning, which is a machine learning approach. In order to improve the lifetime of wireless sensor network, three energy management approaches have been proposed. The first approach is to navigate correctly using reinforcement learning to reduce the length of the routes and to improve energy consumption. The second approach is to exploit a sleep scheduling technique to improve node energy consumption. The last approach is used to restrict data transmission of each node based on the received data change rate. Simulation results show that in terms of network lifespan, the proposed method significantly outperforms previous reported methods.

Spatial registration and track-to-track association (which are mutually coupled) are essential parts in the process of multi-sensor information fusion. The quality of the spatial registration and track association algorithm directly influences the subsequent fusion performance. Aiming to solve the spatial registration and track association problem in the case where incomplete measurements are provided by different sensors, this paper proposes a residual bias estimation registration (RBER) method based on maximum likelihood and the sequential m-best track association algorithm based on the new target density (SMBTANTD). The RBER method realizes the update of incomplete measurements by sequential filtering technology and eliminates the systematic bias of sensors by using information on the significant targets. The SMBTANTD method introduces a new target density in the correlation matrix, which effectively solves the association problem in the scenarios where the numbers of targets measured by multiple sensors are inconsistent. The reported simulation results demonstrate that the proposed algorithm can not only accurately estimate the systematic bias of the sensors, but also significantly improve the performance of track association.

This paper presents a wireless power and downlink data transfer system for medical implants operating over a single 10 MHz inductive link. The system is based on a Carrier Width Modulation (CWM) scheme for high-speed communication and efficient power delivery using a novel modulator circuit design. Unlike conventional modulation techniques, the data rate of the proposed CWM is not limited by the quality factors of the primary and secondary coils. The functionality of the new modulation method is proven using a hybrid implementation comprising a custom-integrated demodulator circuit and board-level discrete components. The proposed Wireless Power and Data Transfer (WPDT) system is also capable of operating under a wide range of data rates. It allows a maximum data rate of 3.33 Mb/s for a maximum power delivery of 6.1 mW at 1 cm coils separation distance. The system can recover more power, reaching 55 mW at 100 kb/s. Due to the system genericity, an operator can select the best compromise between power and data rates in accordance to application or current need, without reconfiguring the receiver. Another advantage of this modulation technique is the simple implementation and the ultra-low power consumption of the CWM demodulator despite its high-speed demodulation.