Wireless communication in the Internet of Things (IoT) is becoming ubiquitous with the large-scale deployment of the fifth-generation (5G) networks. [1] New communication protocols
One of the major challenges faced by passive on-body wireless Internet of Things (IoT) sensors is the absorption of radiated power by tissues in the human body. We present a battery-less, wearable knitted Ultra High Frequency (UHF, 902-928 MHz) Radio Frequency Identification (RFID) compression sensor (Bellypatch) antenna and show its applicability as an on-body respiratory monitor. The antenna radiation efficiency is satisfactory in both free-space and on-body operations. We extract RF (Radio Frequency) sheet resistance values of three knitted silver-coated nylon fabric candidates at 913 MHz. The best type of fabric is selected based on the extracted RF sheet resistance. Simulated and measured performance of the antenna confirm suitability for on-body applications. The proposed Bellypatch antenna is used to measure the breathing activity of a programmable infant patient emulator mannequin (SimBaby) and a human subject. The antenna is highly sensitive to respiratory compression and relaxation. Fluctuations in the backscatter power level/Received Signal Strength Indicator (RSSI) in both cases range from 6 dB to 15 dB. The improved on-body read range of the proposed sensor antenna is 5.8 m, about 10 times higher than its predecessor wearable knitted strain sensing Bellyband antenna (0.6 m). The maximum simulated Specific Absorption Rate (SAR) on a human torso model is 0.25 W/kg, lower than the maximum allowable limit of 1.6 W/kg.
Flexible antennas have the potential to transform wearable and fabric‐based wireless sensing technologies. The antenna discussed in this study is part of a sensing system that uses the back‐scattered power level as the decision metric. For a good wireless sensor, it is necessary to offer a feasible read range and maintain good distinctions in the back‐scattered power levels between the different states (i.e. level of stretch) of the antenna. Moreover, effects due to human body proximity should be minimised. For these reasons, the radiation efficiency is a crucial parameter to investigate. This study presents the radiation efficiency measurement of the proposed flexible knitted ‘Bellyband’ antenna at two different levels of stretch in a reverberation chamber. This work validates the reverberation chamber measurements through comparison with simulations and anechoic chamber measurements at 900 MHz. Moreover, this work demonstrates how the approach can be used to quantify bellyband antenna efficiency in the vicinity of a human body. Finally, the efficiency results were used to predict the read range of Bellyband radio frequency identification technology.
Precise monitoring of respiratory rate in premature newborn infants is essential to initiating medical interventions as required. Wired technologies can be invasive and obtrusive to the patients. We propose a deep-learning-enabled wearable monitoring system for premature newborn infants, where respiratory cessation is predicted using signals that are collected wirelessly from a non-invasive wearable Bellypatch put on the infant’s body. We propose a five-stage design pipeline involving data collection and labeling, feature scaling, deep learning model selection with hyperparameter tuning, model training and validation, and model testing and deployment. The model used is a 1-D convolutional neural network (1DCNN) architecture with one convolution layer, one pooling layer, and three fully-connected layers, achieving 97.15% classification accuracy. To address the energy limitations of wearable processing, several quantization techniques are explored, and their performance and energy consumption are analyzed for the respiratory classification task. Results demonstrate a reduction of energy footprints and model storage overhead with a considerable degradation of the classification accuracy, meaning that quantization and other model compression techniques are not the best solution for respiratory classification problem on wearable devices. To improve accuracy while reducing the energy consumption, we propose a novel spiking neural network (SNN)-based respiratory classification solution, which can be implemented on event-driven neuromorphic hardware platforms. To this end, we propose an approach to convert the analog operations of our baseline trained 1DCNN to their spiking equivalent. We perform a design-space exploration using the parameters of the converted SNN to generate inference solutions having different accuracy and energy footprints. We select a solution that achieves an accuracy of 93.33% with 18x lower energy compared to the baseline 1DCNN model. Additionally, the proposed SNN solution achieves similar accuracy as the quantized model with a 4× lower energy.
The growing research interest in passive RFID (Radio Frequency Identification)-based devices and sensors in a diverse group of applications calls for flexibility in reader antenna performance. We propose a low-cost, easy-to-fabricate, and pattern reconfigurable UHF (Ultra High Frequency) RFID reader antenna in the RFID ISM band (902-928 MHz in the US). The antenna offers a 54 MHz bandwidth (890 -944 MHz) and 8.9 dBi maximum gain. The proposed reconfigurable antenna can radiate four electronically switchable radiation beams in the azimuth plane. The antenna is LHCP (Left Hand Circularly Polarized) with axial ratio (AR) in the ranging from 0.45 dB to 7 dB in the RFID ISM band. Simulation and measurements are presented, and they are in good agreement. The proposed reader array performance is compared against a commercially available reader antenna. The pattern reconfigurable UHF RFID reader antenna not only increases the coverage area for conventional RFID applications but also opens the door to on-body RFID sensor implementation and indoor localization applications.INDEX TERMS IoT (Internet of Things), reader antenna, reconfigurable antenna, RFID (Radio Frequency Identification), UHF (Ultra High Frequency) RFID
We propose an ultra high frequency (UHF) radio frequency identification (RFID, MHz in the US) channel emulation testbed that is capable of simultaneously emulating unique wireless channels. The proposed system can potentially be an invaluable tool in the design and validation of RFIDbased Internet of Things (IoT) sensors and systems. Emulation of ray-tracing-based wireless channels enables the evaluation of inherently difficult and complex radio frequency (RF) scenarios, particularly in situations when in-person experimentation is not feasible or desirable (e.g., during a pandemic or in a critical care facility). Furthermore, the emulation testbed is able to generate a large amount of sensor data in a limited time period. Machine learning techniques used in wireless IoT can be greatly enhanced by a large amount of data extracted from the emulation of dynamic and challenging environments. The proposed multi-channel emulation testbed is therefore a valuable solution for experimentation on real hardware and a convenient tool for pre-clinical-trial system validation.INDEX TERMS Internet of Things (IoT), passive sensors, Radio Frequency Identification (RFID), Ultra High Frequency (UHF), Wireless channel emulation,
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