Radio Frequency Identification (RFID) sensors, integrating the features of Wireless Information and Power Transfer (WIPT), object identification and energy efficient sensing capabilities, have been considered a new paradigm of sensing and communication for the futuristic information systems. RFID sensor tags featuring contactless sensing, wireless information transfer, wireless powered, light weight, non-line-of-sight transmission, flexible and pasteable are a critical enabling technology for future Internet-of-Things (IoT) applications, such as manufacturing, logistics, healthcare, agriculture and food. They have attracted numerous research efforts due to their innovative potential in the various application fields. However, there has been a gap between the in-lab investigations and the practical IoT application scenarios, which has motivated this survey of this research to identify the promising enabling techniques and the underlying challenges. This study aims to provide an exhaustive review on the state-of-art RFID sensor technologies from the system implementation perspective by focusing on the fundamental RF energy harvesting theories, the recent technical progresses and commercial solutions, innovative applications and some RFID sensor based IoT solutions, identify the underlying technological challenges at the time being, and give the future research trends and promising application fields in the rich sensing applications of the forthcoming IoT era.

Radio Frequency Identification (RFID) has become a key enabler for the Industrial Internet of Things (IIoT). By taking advantage of the backscattered radio waves, the identity and position of RFID-labeled objects can be obtained simultaneously. Very many efforts have been devoted to the investigations of RFID positioning, but practical applications are rarely seen in industrial sectors. Motived by the potentiality of RFID positioning and the gap between research and practice, this investigation aims to provide an in-depth survey of the methods and the latest technical progress in RFID positioning to identify the underlying challenges. The main contributions include (1) A theoretical model of radio frequency backscatter coupling-based positioning and an in-depth analysis of the influencing parameters. (2) A new classification of RFID positioning techniques with a discussion of the latest progress. (3) A summary of the potential applications and the underlying challenges facing the state-of-the-art, including the simultaneous attitude and position acquisition and the flexible integration with practical applications. (4) The prospects of emerging technologies such as the integration of identification-sensing-positioning, RFID positioning-powered robotics operations, and machine learning/deep learning algorithms are discussed. This investigation gives a comprehensive perspective on RFID positioning, which provides a reference for related research and practices.

The Radio Frequency Identification (RFID) technology has gained interests in both academia and industry since its invention. In addition to the applications in access control and supply chain, RFID is also a cost-efficient solution for Non-Destructive Testing (NDT) and pervasive monitoring. The battery free RFID tags are used as independent electromagnetic sensors or energy harvesting and data transmission interface of sensor modules for different measurement purposes. This review paper aims to provide a comprehensive overview of the innovative designs and applications of RFID sensor technology with new insights, identify the technical challenges, and outline the future perspectives. With a brief introduction to the fundamentals of RFID measurement, the enabling technologies and recent technical progress are illustrated, followed by an extensive discussion of the novel designs and applications. Then, based on an in-depth analysis, the potential constraints are identified and the envisaged future directions are suggested, including printable/wearable RFID, System-on-Chip (SoC), ultra-low power, etc. The comprehensive discussion of RFID sensor technology will be inspirational and useful for academic and industrial communities in investigating, developing, and applying RFID for various measurement applications.

Without dependence on external anchors, the micro-electro-mechanical system inertial measurement unit (MIMU) allowing autonomous localization has promised great potential in wearable IoT applications, including kinematic analysis in sports, medical treatment, elderly care, and disaster rescue. However, the miniaturization of devices for unobtrusive sensing, algorithms minimizing the inherent accumulative errors of inertial devices, and the adaptivity of algorithms for various motion modalities are the key challenges. The removal of accumulative error in the continuous gait cycles with adaptive algorithms is a critical issue regarding localization accuracy, especially for low-cost devices. This investigation proposes an adaptive threshold-based zero-velocity update (ZUPT) algorithm to separate the timing of gait cycle phases and compensate for the residual velocity with a linear fitting approximation. The key contributions include: 1) a lightweight threshold-based zero-velocity detection algorithm to split the gait cycle phases of continuous walking; 2) a quaternion-based extended Kalman filter (EKF) algorithm to reduce the errors of the nonlinear operations for attitude prediction; 3) a linear fitting method for compensating the residual velocity in each gait cycle of continuous walking; and 4) the design of a miniature single-MIMU-based foot-mountable wearable device and the corresponding experimental studies to verify the proposed methods. Results show that the relative error is less than 3.0% for 2-D and 3-D trajectories, and the tests with different locomotion patterns demonstrate the adaptivity of the proposed algorithm compared to its peers. The results show that the presented techniques are capable of handling accumulative errors for low-cost MIMU-based systems with good adaptivity.

The recent scientific and technical advances in Internet of Things (IoT) based pervasive sensing and computing have created opportunities for the continuous monitoring of human activities for different purposes. The topic of human activity recognition (HAR) and motion analysis, due to its potentiality in human–machine interaction (HMI), medical care, sports analysis, physical rehabilitation, assisted daily living (ADL), children and elderly care, has recently gained increasing attention. The emergence of some novel sensing devices featuring miniature size, a light weight, and wireless data transmission, the availability of wireless communication infrastructure, the progress of machine learning and deep learning algorithms, and the widespread IoT applications has promised new opportunities for a significant progress in this particular field. Motivated by a great demand for HAR-related applications and the lack of a timely report of the recent contributions to knowledge in this area, this investigation aims to provide a comprehensive survey and in-depth analysis of the recent advances in the diverse techniques and methods of human activity recognition and motion analysis. The focus of this investigation falls on the fundamental theories, the innovative applications with their underlying sensing techniques, data fusion and processing, and human activity classification methods. Based on the state-of-the-art, the technical challenges are identified, and future perspectives on the future rich, sensing, intelligent IoT world are given in order to provide a reference for the research and practices in the related fields.

Wearable indoor localization can now find applications in a wide spectrum of fields, including the care of children and the elderly, sports motion analysis, rehabilitation medicine, robotics navigation, etc. Conventional inertial measurement unit (IMU)-based position estimation and radio signal indoor localization methods based on WiFi, Bluetooth, ultra-wide band (UWB), and radio frequency identification (RFID) all have their limitations regarding cost, accuracy, or usability, and a combination of the techniques has been considered a promising way to improve the accuracy. This investigation aims to provide a cost-effective wearable sensing solution with data fusion algorithms for indoor localization and real-time motion analysis. The main contributions of this investigation are: (1) the design of a wireless, battery-powered, and light-weight wearable sensing device integrating a low-cost UWB module-DWM1000 and micro-electromechanical system (MEMS) IMU-MPU9250 for synchronized measurement; (2) the implementation of a Mahony complementary filter for noise cancellation and attitude calculation, and quaternions for frame rotation to obtain the continuous attitude for displacement estimation; (3) the development of a data fusion model integrating the IMU and UWB data to enhance the measurement accuracy using Kalman-filter-based time-domain iterative compensations; and (4) evaluation of the developed sensor module by comparing it with UWB- and IMU-only solutions. The test results demonstrate that the average error of the integrated module reached 7.58 cm for an arbitrary walking path, which outperformed the IMU- and UWB-only localization solutions. The module could recognize lateral roll rotations during normal walking, which could be potentially used for abnormal gait recognition.