This paper focuses on the feasibility of tracking the chest wall movement of a human subject during respiration from the waveforms recorded using an impulse-radio (IR) ultra-wideband radar. The paper describes the signal processing to estimate sleep apnea detection and breathing rate. Some techniques to solve several problems in these types of measurements, such as the clutter suppression, body movement and body orientation detection are described. Clutter suppression is achieved using a moving averaging filter to dynamically estimate it. The artifacts caused by body movements are removed using a threshold method before analyzing the breathing signal. The motion is detected using the time delay that maximizes the received signal after a clutter removing algorithm is applied. The periods in which the standard deviations of the time delay exceed a threshold are considered macro-movements and they are neglected. The sleep apnea intervals are detected when the breathing signal is below a threshold. The breathing rate is determined from the robust spectrum estimation based on Lomb periodogram algorithm. On the other hand the breathing signal amplitude depends on the body orientation respect to the antennas, and this could be a problem. In this case, in order to maximize the signal-to-noise ratio, multiple sensors are proposed to ensure that the backscattered signal can be detected by at least one sensor, regardless of the direction the human subject is facing. The feasibility of the system is compared with signals recorded by a microphone.

In this article, an overview of recent advances in the field of battery-less near-field communication (NFC) sensors is provided, along with a brief comparison of other short-range radio-frequency identification (RFID) technologies. After reviewing power transfer using NFC, recommendations are made for the practical design of NFC-based tags and NFC readers. A list of commercial NFC integrated circuits with energy-harvesting capabilities is also provided. Finally, a survey of the state of the art in NFC-based sensors is presented, which demonstrates that a wide range of sensors (both chemical and physical) can be used with this technology. Particular interest arose in wearable sensors and cold-chain traceability applications. The availability of low-cost devices and the incorporation of NFC readers into most current mobile phones make NFC technology key to the development of green Internet of Things (IoT) applications.

A low-cost chipless RFID sensor tag to determine the dielectric properties of materials is presented. The sensor is based on a depolarizing dipole resonator loaded with printed capacitors whose capacitance depends on the permittivity of the material located on contact with the tag. The permittivity is obtained from the shift of the resonance frequency. The resonator is tested both individually as well as placed on a periodic arrangement so as to form a frequency selective structure (FSS) to increase the cross- polar radar cross section, thus facilitating the tag detection. The proposed structure and measurement procedure is particularly suitable for the characterization of civil materials as a non-destructive testing (NDT).

A novel moisture sensor based on Near Field Communication (NFC) is presented. The system consists of a battery-less sensing device which measures temperature, relative humidity and the volumetric water content, and is powered from the magnetic field generated by the reader. In order to compute the sensed data, the system includes a microcontroller, which has been programmed to make the calculations and to send the processed data to the NFC chip through an I2C bus. This data is stored into the EEPROM memory of the NFC integrated circuit in NFC Data Exchange Format (NDEF), which is read by the reader. An analysis of different methods to measure the soil moisture is presented in order to select the approach which fits better with the constraints of a NFC energy harvested system.

This article reviews the recent advances in the field of batteryless near-field communication (NFC) sensors for chemical sensing and biosensing. The commercial availability of low-cost commercial NFC integrated circuits (ICs) and their massive integration in smartphones, used as readers and cloud interfaces, have aroused great interest in new batteryless NFC sensors. The fact that coil antennas are not importantly affected by the body compared with other wireless sensors based on far-field communications makes this technology suitable for future wearable point-of-care testing (PoCT) devices. This review first compares energy harvesting based on NFC to other energy-harvesting technologies. Next, some practical recommendations for designing and tuning NFC-based tags are described. Power transfer is key because in most cases, the energy harvested has to be stable for several seconds and not contaminated by undesired signals. For this reason, the effect of the dimensions of the coils and the conductivity on the wireless power transfer is thoroughly discussed. In the last part of the review, the state of the art in NFC-based chemical and biosensors is presented. NFC-based tags (or sensor tags) are mainly based on commercial or custom NFC ICs, which are used to harvest the energy from the RF field generated by the smartphone to power the electronics. Low-consumption colorimeters and potentiostats can be integrated into these NFC tags, opening the door to the integration of chemical sensors and biosensors, which can be harvested and read from a smartphone. The smartphone is also used to upload the acquired information to the cloud to facilitate the internet of medical things (IoMT) paradigm. Finally, several chipless sensors recently proposed in the literature as a low-cost alternative for chemical applications are discussed.

This paper presents a color-based classification system for grading the ripeness of fruit using a battery-less Near Field Communication (NFC) tag. The tag consists of a color sensor connected to a low-power microcontroller that is connected to an NFC chip. The tag is powered by the energy harvested from the magnetic field generated by a commercial smartphone used as a reader. The raw RGB color data measured by the colorimeter is converted to HSV (hue, saturation, value) color space. The hue angle and saturation are used as features for classification. Different classification algorithms are compared for classifying the ripeness of different fruits in order to show the robustness of the system. The low cost of NFC chips means that tags with sensing capability can be manufactured economically. In addition, nowadays, most commercial smartphones have NFC capability and thus a specific reader is not necessary. The measurement of different samples obtained on different days is used to train the classification algorithms. The results of training the classifiers have been saved to the cloud. A mobile application has been developed for the prediction based on a table-based method, where the boundary decision is downloaded from a cloud service for each product. High accuracy, between 80 and 93%, is obtained depending on the kind of fruit and the algorithm used.