A hierarchical core-shell tribopositive yarn TENG with electrospun polyethyleneimine (PEI) nanofibers for sustainable energy harvesting and human machine interface application

Dealing with issues related to safety of Nuclear Power Plants (NPPs) is of high importance and priority for assuring nonstop energy production. To enhance safety, modernization and upgrade of the aging installations by incorporating automation processes is unavoidable for many reasons, among them, environmental protection and lifetime extension of currently operating NPP. In that context, Human Machine Interface (HMI) applications are a subject of thorough study. The aim of this work is to develop a mechanism to evaluate the efficiency of HMI in nuclear power plant safety. To that end, HMI applications are addressed as a single joint system and they are not seen separately as a human and machine part. The proposed evaluator was implemented by utilizing Fuzzy Logic and holistic approaches. The implementation and all the experiments were conducted in Matlab by using the fuzzy toolbox.

The human forearm is one of the most densely distributed parts of the human body, with the most irregular spatial distribution of muscles. A number of specific forearm muscles control hand motions. Acquiring high-fidelity sEMG signals from human forearm muscles is vital for human-machine interface (HMI) applications based on gesture recognition. Currently, the most commonly used commercial electrodes for detecting sEMG or other electrophysiological signals have a rigid nature without stretchability and cannot maintain conformal contact with the human skin during deformation, and the adhesive hydrogel used in them to reduce skin-electrode impedance may shrink and cause skin inflammation after long-term use. Therefore, developing elastic electrodes with stretchability and biocompatibility for sEMG signal recording is essential for developing HMI. Here, we fabricated a nanocomposite hybrid on-skin electrode by infiltrating silver nanowires (AgNWs), a one-dimensional (1D) nano metal material with conductivity, into polydimethylsiloxane (PDMS), a silicone elastomer with a similar Young's modulus to that of the human skin. The AgNW on-skin electrode has a thickness of 300 μm and low sheet resistance of 0.481 ± 0.014 Ω/sq and can withstand the mechanical strain of up to 54% and maintain a sheet resistance lower than 1 Ω/sq after 1000 dynamic strain cycles. The AgNW on-skin electrode can record high signal-to-noise ratio (SNR) sEMG signals from forearm muscles and can reflect various force levels of muscles by sEMG signals. Besides, four typical hand gestures were recognized by the multichannel AgNW on-skin electrodes with a recognition accuracy of 92.3% using machine learning method. The AgNW on-skin electrode proposed in this study has great potential and promise in various HMI applications that employ sEMG signals as control signals.

In-ear acquisition of physiological signals, such as electromyography (EMG), electrooculography (EOG), electroencephalography (EEG), and electrocardiography (ECG), is a promising approach to mobile health (mHealth) due to its non-invasive and user-friendly nature. By providing a convenient and comfortable means of physiological signal monitoring, in-ear signal acquisition could potentially increase patient compliance and engagement with mHealth applications. The development of reliable and comfortable soft dry in-ear electrode systems could, therefore, have significant implications for both mHealth and human-machine interface (HMI) applications. This research evaluates the quality of the ECG signal obtained with soft dry electrodes inserted in the ear canal. An earplug with six soft dry electrodes distributed around its perimeter was designed for this study, allowing for the analysis of the signal coming from each electrode independently with respect to a common reference placed at different positions on the body of the participants. An analysis of the signals in comparison with a reference signal measured on the upper right chest (RA) and lower left chest (LL) was performed. The results show three typical behaviors for the in-ear electrodes. Some electrodes have a high correlation with the reference signal directly after inserting the earplug, other electrodes need a settling time of typically 1-3 min, and finally, others never have a high correlation. The SoftPulseTM electrodes used in this research have been proven to be perfectly capable of measuring physiological signals, paving the way for their use in mHealth or HMI applications. The use of multiple electrodes distributed in the ear canal has the advantage of allowing a more reliable acquisition by intelligently selecting the signal acquisition locations or allowing a better spatial resolution for certain applications by processing these signals independently.

In this study, a proof of concept for seamless energy flow is demonstrated by converting light energy into electrical energy and then storing it. A simple heterojunction structure of an FTO/ZnO/NiO/AgNWs/ZnO array transparent photovoltaic (TPV) device is employed to ensure an excellent average visible transmittance value of 67.7% while storing light energy as electrical energy in a capacitor bank. By simple and stable array connection of unit cell devices, the power leakage is minimized while maximizing output voltage. In the array TPV device, an open‐circuit voltage of 1.4 V is achieved under 365 nm illumination, with a voltage of 1.26 V stored in the capacitor bank, accumulating to over 6 V. The stored electrical energy is successfully converted for use by an light‐emitting diode (LED) light source, demonstrating sustained light‐up for over 30 s. This work explores facile energy generation, storage and utilization through TPVs, with a good potential for transparent energy harvesting and human interface applications.

This paper provides a short review of sustainable hybrid energy harvesting and its applications. The potential usage of self-powered wireless sensor (WSN) systems has recently drawn a lot of attention to sustainable energy harvesting. The objective of this research is to determine the potential of hybrid energy harvesters to help single energy harvesters overcome their energy deficiency problems. The major findings of the study demonstrate how hybrid energy harvesting, which integrates various energy conversion technologies, may increase power outputs, and improve space utilization efficiency. Hybrid energy harvesting involves collecting energy from multiple sources and converting it into electrical energy using various transduction mechanisms. By properly integrating different energy conversion technologies, hybridization can significantly increase power outputs and improve space utilization efficiency. Here, we present a review of recent progress in hybrid energy-harvesting systems for sustainable green energy harvesting and their applications in different fields. This paper starts with an introduction to hybrid energy harvesting, showing different hybrid energy harvester configurations, i.e., the integration of piezoelectric and electromagnetic energy harvesters; the integration of piezoelectric and triboelectric energy harvesters; the integration of piezoelectric, triboelectric, and electromagnetic energy harvesters; and others. The output performance of common hybrid systems that are reported in the literature is also outlined in this review. Afterwards, various potential applications of hybrid energy harvesting are discussed, showing the practical attainability of the technology. Finally, this paper concludes by making recommendations for future research to overcome the difficulties in developing hybrid energy harvesters. The recommendations revolve around improving energy conversion efficiency, developing advanced integration techniques, and investigating new hybrid configurations. Overall, this study offers insightful information on sustainable hybrid energy harvesting together with quantitative information, numerical findings, and useful research recommendations that progress and promote the use of this technology.

Today’s commercial Internet of Things devices remain largely dependent upon batteries, which offer high capacity, stable energy storage at the expense of limited shelf-lives and toxic chemical compositions. Research on sustainable energy harvesting platforms is essential to realizing a new generation of long-lived and environmentally friendly IoT products. This paper contributes to this goal by introducing AsTAR, an energy-aware task scheduler and associated reference platform that aims to lower the burden of developing sustainable applications through self-adaptive task scheduling. We evaluate AsTAR based on its capability to deliver sustainable operation on heterogeneous platforms. Evaluation shows that: (i.) With zero modeling AsTAR rapidly identifies optimum task scheduling rates, while (ii.) reacting quickly to environmental change and (iii.) these features incur minimal performance overhead in terms of memory, computation and energy. Considered in sum, we believe that these features significantly simplify the process of creating sustainable energy harvesting IoT applications.

In this article, a novel energy harvesting (EH) interface for a flexible thin-film piezoelectric generator (FPEG) is proposed for EH from irregular human motion. The traditional thick piezoelectric generator (PEG) based kinetic EH circuits are designed for continuous and sinusoidal inputs from the cantilever-based structures and are not suitable for EH from irregular human motion. The proposed EH interface circuit significantly enhances energy extraction with a load-screening scheme, which minimizes the load capacitance to maximize the PEG output voltage up to 102 V while using the standard voltage 0.18- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> m process. An energy-aware wake-up controller is designed to (monitor and) detect the FPEG deformation to assure that the harvesting interface is only activated when enough energy is available for EH. When the FPEG voltage peaks, the energy is transferred to the battery through an inductor with a single-cycle buck-converter-like operation, allowing the input voltage and frequency-independent EH operation. The measurement results show that the proposed EH interface successfully harvests energy from irregular pulsed inputs with 562% improvement compared with a full-bridge rectifier.

High-precision strategy for piezoelectric characterization of nano/microwire

Chapter 9 - Recent advancement in sustainable energy harvesting using piezoelectric materials

Ultrasound imaging technology has recently been proven to achieve higher classification accuracies than surface electromyography when predicting hand motions. However, typical designs involve a large linear array ultrasonic probe or bulky multichannel ultrasonic transducers. In this study, we constructed wearable ultrasonic sensors (WUS) using 110$-\mu$m thick flexible piezoelectric polymer film for an ergonomic strategy for prosthetic and human machine interface applications. We attached the three WUSs on the forearm of a healthy subject, 5 cm away from the wrist, to monitor the tissue motions associated with the finger flexions. An experiment to predict 100 ms time intervals of individual finger flexions was investigated using novel feature extraction methods involving the discrete wavelet transform. We achieved an accuracy of 92.5±7.6% for classification of finger flexions using a multilayer perceptron with a hidden layer of 15 nodes. The F1 score for classifying the five fingers ranged between 86-99% across all fingers using uniformly distributed class sample sizes. The results strongly support the utility of the ergonomic WUS system for continuously predicting individual finger flexions in prosthetic and human machine interface applications.

A Dynamic Connection Scheme for User Interface of Process Control System in Offshore Plant

Surface electromyogram (sEMG) has found wide range of applications in human machine interface, assistive technology and health monitoring. A simple autoregressive (AR) model may be used to describe the shape of the signal spectrum. Then, the main concern is the manner in which the residual signal of the AR model is parameterized. It has been shown only recently that the sEMG signals exhibit heteroscedasticity resulting in the AR residual signal being heteroscedastic. In this paper, the aim is to explore the effect of using different order AR models with different residual signal models on sEMG-based classification of hand activities. It is demonstrated that the appropriateness of the AR model order should be determined by jointly testing the AR and residual model parameters for classification in terms of the accuracy that they provide. A stand-alone statistical test for determining the AR model order may not correspond with the accuracy that the model would provide when used in conjunction with the features extracted from the residual signals. Moreover, feature selection is essential while testing large number of features to determine the appropriateness of the signal models used.

Speech is an advanced signal consisting of varied data, regarding the message to be communicated, speaker, language, region, emotions etc. Speech process is one among the vital branches of digital signal processing and finds applications in Human Machine interface, Telecommunication, Audio mining, Security etc., Speech recognition is vital for natural interaction between human and machine. In speech emotion recognition, the emotion state of a speaker is extracted from his or her speech. The acoustic characteristic of the speech signal is Feature. Feature extraction is the method that extracts a little quantity of information from the speech signal that may later be used to represent speaker. Several feature extraction strategies are implemented as of now and Mel Frequency Cepstral coefficient (MFCC). This paper presents speaker emotions recognized by using the information extracted from the speaker speech signal. Mel Frequency Cepstral coefficient (MFCC) technique is employed to acknowledge feeling of a speaker from their voice. The designed system was implemented for Happy, sad and anger emotions and the potency was found to be about 82% for sad, 74% for angry, and 72% for happy.

Self-powered and degradable humidity sensors based on silk nanofibers and its wearable and human–machine interaction applications

The past few decades have witnessed the tremendous progress of human–machine interface (HMI) in communication, education, and manufacturing fields. However, due to signal acquisition devices’ limitations, the research on HMI related to communication aid applications for the disabled is progressing slowly. Here, inspired by frogs’ croaking behavior, a bionic triboelectric nanogenerator (TENG)‐based ultra‐sensitive self‐powered electromechanical sensor for muscle‐triggered communication HMI application is developed. The sensor possesses a high sensitivity (54.6 mV mm−1), a high‐intensity signal (± 700 mV), and a wide sensing range (0–5 mm). The signal intensity is 206 times higher than that of traditional biopotential electromyography methods. By leveraging machine learning algorithms and Morse code, the safe, accurate (96.3%), and stable communication aid HMI applications are achieved. The authors' bionic TENG‐based electromechanical sensor provides a valuable toolkit for HMI applications of the disabled, and it brings new insights into the interdisciplinary cross‐integration between TENG technology and bionics.