The last decade has witnessed significant advances in energy harvesting technologies as a possible alternative to provide a continuous power supply for small, low-power devices in applications, such as wireless sensing, data transmission, actuation, and medical implants. Piezoelectric energy harvesting (PEH) has been a salient topic in the literature and has attracted widespread attention from researchers due to its advantages of simple architecture, high power density, and good scalability. This paper presents a comprehensive review on the state-of-the-art of piezoelectric energy harvesting. Various key aspects to improve the overall performance of a PEH device are discussed, including basic fundamentals and configurations, materials and fabrication, performance enhancement mechanisms, applications, and future outlooks.

A comprehensive review on piezoelectric energy harvesting technology: Materials, mechanisms, and applications

Nowadays, great effort has been devoted to establishing wearable electronics with excellent stretchability, high sensitivity, good mechanical strength, and multifunctional characteristics. Herein, a soft conductive hydrogel is rationally designed by proportionally mixing silk fibroin, polyacrylamide, graphene oxide, and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate). The resultant hydrogel has considerable stretchability and compressibility, which enables it to be assembled into a strain/pressure sensor with a wide sensing range (strain, 2%-600%; pressure, 0.5-119.4 kPa) and reliable stability. Then, the corresponding sensor is capable of monitoring a series of physical signals of the human body (e.g., joint movement, facial gesture, pulse, breathing, etc.). In particular, the hydrogel-based sensor is biocompatible, with no anaphylactic reaction on human skin. More interestingly, this conductive hydrogel exhibits a positive response when it works in a triboelectric nanogenerator; consequently, it lights up 20 commericial green light-emitting diodes. Thus, this silk fibroin-based hydrogel is a kind of multifunctional material toward wearable electronics with versatile applications in health and exercise monitors, soft robots, and power sources.

Stretchable, Biocompatible, and Multifunctional Silk Fibroin-Based Hydrogels toward Wearable Strain/Pressure Sensors and Triboelectric Nanogenerators.

Sensors based on the detection of small resistance variations are universally recognized as piezoresistive. Being one of the simplest, most common and most investigated classes of sensors, continuous efforts are focused on creating improved devices with higher performance that can be used in many commercial and non–commercial applications (e.g. evaluation of strain, pressure, acceleration, force etc.). Consequently, despite the fact that more than 150 years have passed since the discovery of the piezoresistive effect in some classes of metals and semiconductors, the development of such sensors remains interesting and topical. Moreover, with the advent of second–generation robotics, research on piezoresistive sensors has undergone a massive increase. This paper aims to be a short, self–consistent vademecum which would be useful to researchers and engineers, since it focuses on the fundamentals of theory, materials, and readout–circuit design pertinent to the most recent developments in the field of piezoresistive sensors.

Theory, technology and applications of piezoresistive sensors: A review

Technology has become ubiquitous, it is all around us and is becoming part of us. Togetherwith the rise of the Internet of Things (IoT) paradigm and enabling technologies (e.g., Augmented Reality (AR), Cyber-Physical Systems, Artificial Intelligence (AI), blockchain or edge computing), smart wearables and IoT-based garments can potentially have a lot of influence by harmonizing functionality and the delight created by fashion. Thus, smart clothes look for a balance among fashion, engineering, interaction, user experience, cybersecurity, design and science to reinvent technologies that can anticipate needs and desires. Nowadays, the rapid convergence of textile and electronics is enabling the seamless and massive integration of sensors into textiles and the development of conductive yarn. The potential of smart fabrics, which can communicate with smartphones to process biometric information such as heart rate, temperature, breathing, stress, movement, acceleration, or even hormone levels, promises a new era for retail. This article reviews the main requirements for developing smart IoT-enabled garments and shows smart clothing potential impact on business models in the medium-term. Specifically, a global IoT architecture is proposed, the main types and components of smart IoT wearables and garments are presented, their main requirements are analyzed and some of the most recent smart clothing applications are studied. In this way, this article reviews the past and present of smart garments in order to provide guidelines for the future developers of a network where garments will be connected like other IoT objects: the Internet of Smart Clothing.

https://www.mdpi.com/2079-9292/7/12/405/pdf

Towards The Internet of Smart Clothing: A Review on IoT Wearables and Garments for Creating Intelligent Connected E-Textiles

Nanomaterial-enabled flexible and stretchable electronics have seen tremendous progress in recent years, evolving from single sensors to integrated sensing systems. Compared with nanomaterial-enabled sensors with a single function, integration of multiple sensors is conducive to comprehensive monitoring of personal health and environment, intelligent human-machine interfaces, and realistic imitation of human skin in robotics and prosthetics. Integration of sensors with other functional components promotes real-world applications of the sensing systems. Here, an overview of the design and integration strategies and manufacturing techniques for such sensing systems is given. Then, representative nanomaterial-enabled flexible and stretchable sensing systems are presented. Following that, representative applications in personal health, fitness tracking, electronic skins, artificial nervous systems, and human-machine interactions are provided. To conclude, perspectives on the challenges and opportunities in this burgeoning field are considered.

Nanomaterial-Enabled Flexible and Stretchable Sensing Systems: Processing, Integration, and Applications

Wearable health monitoring systems have gained considerable interest in recent years owing to their tremendous promise for personal portable health watching and remote medical practices. The sensors with excellent flexibility and stretchability are crucial components that can provide health monitoring systems with the capability of continuously tracking physiological signals of human body without conspicuous uncomfortableness and invasiveness. The signals acquired by these sensors, such as body motion, heart rate, breath, skin temperature and metabolism parameter, are closely associated with personal health conditions. This review attempts to summarize the recent progress in flexible and stretchable sensors, concerning the detected health indicators, sensing mechanisms, functional materials, fabrication strategies, basic and desired features. The potential challenges and future perspectives of wearable health monitoring system are also briefly discussed.

Flexible, Stretchable Sensors for Wearable Health Monitoring: Sensing Mechanisms, Materials, Fabrication Strategies and Features

Field programmable gate array (FPGA) is widely considered as a promising platform for convolutional neural network (CNN) acceleration. However, the large numbers of parameters of CNNs cause heavy computing and memory burdens for FPGA-based CNN implementation. To solve this problem, this paper proposes an optimized compression strategy, and realizes an accelerator based on FPGA for CNNs. Firstly, a reversed-pruning strategy is proposed which reduces the number of parameters of AlexNet by a factor of 13× without accuracy loss on the ImageNet dataset. Peak-pruning is further introduced to achieve better compressibility. Moreover, quantization gives another 4× with negligible loss of accuracy. Secondly, an efficient storage technique, which aims for the reduction of the whole overhead cache of the convolutional layer and the fully connected layer, is presented respectively. Finally, the effectiveness of the proposed strategy is verified by an accelerator implemented on a Xilinx ZCU104 evaluation board. By improving existing pruning techniques and the storage format of sparse data, we significantly reduce the size of AlexNet by 28×, from 243 MB to 8.7 MB. In addition, the overall performance of our accelerator achieves 9.73 fps for the compressed AlexNet. Compared with the central processing unit (CPU) and graphics processing unit (GPU) platforms, our implementation achieves 182.3× and 1.1× improvements in latency and throughput, respectively, on the convolutional (CONV) layers of AlexNet, with an 822.0× and 15.8× improvement for energy efficiency, separately. This novel compression strategy provides a reference for other neural network applications, including CNNs, long short-term memory (LSTM), and recurrent neural networks (RNNs).

Optimized Compression for Implementing Convolutional Neural Networks on FPGA

In this paper, we present a new “hybrid” method for on-wafer dielectric measurements of nanolitre fluid samples. The first part of the hybrid method uses a technique that extracts the complex relative permittivity of the material used to make microfluidic channels. The extraction is performed with an empty channel measurement, and thus requires no extra deembedding structures. The second part of the hybrid method involves an accurate extraction of the complex relative permittivity of dielectric fluids. The hybrid method uses three different extraction algorithms and calculates their Type B uncertainties within the NIST Microwave Uncertainty Framework. By choosing the calculation algorithm with the smallest uncertainty at each frequency, the hybrid method can achieve accurate measurements of the fluids’ permittivity over a broad bandwidth. One of the three algorithms is a new algorithm based on closed-form equations. A trace-based algorithm is also applied to fluids measurements, for the first time to our knowledge. Through the uncertainty analysis, we found out that these two algorithms should be favored over a traditional least-squares optimization-based algorithm at millimeter wave frequencies, due to their lower sensitivities to probe-placement errors.

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Hybrid Characterization of Nanolitre Dielectric Fluids in a Single Microfluidic Channel Up to 110 GHz

The field of piezoresistive sensors has been undergoing a significant revolution in terms of design methodology, material technology and micromachining process. However, the temperature dependence of sensor characteristics remains a hurdle to cross. This review focuses on the issues in thermal-performance instability of piezoresistive sensors. Based on the operation fundamental, inducements to the instability are investigated in detail and correspondingly available ameliorative methods are presented. Pros and cons of each improvement approach are also summarized. Though several schemes have been proposed and put into reality with favorable achievements, the schemes featuring simple implementation and excellent compatibility with existing techniques are still emergently demanded to construct a piezoresistive sensor with excellent comprehensive performance.

https://www.mdpi.com/1424-8220/16/12/1984/pdf

Thermal-Performance Instability in Piezoresistive Sensors: Inducement and Improvement

This letter proposes a three-dimensional piezoelectric energy harvester (3D PEH) that enables energy scavenging from vibrations in arbitrary directions. The proposed harvester comprises a spring, a primary beam, an inner beam, and two magnetic masses. The beams capture vibrations along their surface normal directions, and the spring, which acts as a mechanical energy buffer, is used to sense vibrations that occur parallel to the length direction of the inner beam. Magnetic coupling is introduced to perform frequency up-conversion and thus transfer vibration energy from the inner beam to the primary beam. As a benefit of the proposed structure, the 3D PEH can sense and convert all vibration energies occurring within the 3D space. When compared with use of the same beams in a linear configuration, the peak voltage of the proposed configuration is more than 3 V, and the bandwidths of voltages exceeding 5 V are wider by more than 3 Hz. Simultaneously, the primary beam resonant frequency is reduced by more tha...

Wei Zhao

Papers

Flexible, Stretchable Sensors for Wearable Health Monitoring: Sensing Mechanisms, Materials, Fabrication Strategies and Features

Optimized Compression for Implementing Convolutional Neural Networks on FPGA

Hybrid Characterization of Nanolitre Dielectric Fluids in a Single Microfluidic Channel Up to 110 GHz

Thermal-Performance Instability in Piezoresistive Sensors: Inducement and Improvement

Three-dimensional piezoelectric energy harvester with spring and magnetic coupling