A Self-Sensing and Self-Powered Wearable System Based on Multi-Source Human Motion Energy Harvesting.

Wearable devices play an indispensable role in modern life, and the human body contains multiple wasted energies available for wearable devices. This study proposes a self-sensing and self-powered wearable system (SS-WS) based on scavenging waist motion energy and knee negative energy. The proposed SS-WS consists of a three-degree-of-freedom triboelectric nanogenerator (TDF-TENG) and a negative energy harvester (NEH). The TDF-TENG is driven by waist motion energy and the generated triboelectric signals are processed by deep learning for recognizing the human motion. The triboelectric signals generated by TDF-TENG can accurately recognize the motion state after processing based on Gate Recurrent Unit deep learning model. With double frequency up-conversion, the NEH recovers knee negative energy generation for powering wearable devices. A model wearing the single energy harvester can generate the power of 27.01 mW when the movement speed is 8 km h-1 , and the power density of NEH reaches 0.3 W kg-1 at an external excitation condition of 3 Hz. Experiments and analysis prove that the proposed SS-WS can realize self-sensing and effectively power wearable devices.

Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG.

The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver's brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety.

A High-Speed Target-Free Vision-Based Sensor for Bus Rapid Transit Viaduct Vibration Measurements Using CMT and ORB Algorithms.

Bus Rapid Transit (BRT) has become an increasing source of concern for public transportation of modern cities. Traditional contact sensing techniques during the process of health monitoring of BRT viaducts cannot overcome the deficiency that the normal free-flow of traffic would be blocked. Advances in computer vision technology provide a new line of thought for solving this problem. In this study, a high-speed target-free vision-based sensor is proposed to measure the vibration of structures without interrupting traffic. An improved keypoints matching algorithm based on consensus-based matching and tracking (CMT) object tracking algorithm is adopted and further developed together with oriented brief (ORB) keypoints detection algorithm for practicable and effective tracking of objects. Moreover, by synthesizing the existing scaling factor calculation methods, more rational approaches to reducing errors are implemented. The performance of the vision-based sensor is evaluated through a series of laboratory tests. Experimental tests with different target types, frequencies, amplitudes and motion patterns are conducted. The performance of the method is satisfactory, which indicates that the vision sensor can extract accurate structure vibration signals by tracking either artificial or natural targets. Field tests further demonstrate that the vision sensor is both practicable and reliable.

A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation.

In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver's EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver's vigilance level. Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model.

A Novel Multi-Sensor Environmental Perception Method Using Low-Rank Representation and a Particle Filter for Vehicle Reversing Safety.

Environmental perception and information processing are two key steps of active safety for vehicle reversing. Single-sensor environmental perception cannot meet the need for vehicle reversing safety due to its low reliability. In this paper, we present a novel multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. The proposed system consists of four main steps, namely multi-sensor environmental perception, information fusion, target recognition and tracking using low-rank representation and a particle filter, and vehicle reversing speed control modules. First of all, the multi-sensor environmental perception module, based on a binocular-camera system and ultrasonic range finders, obtains the distance data for obstacles behind the vehicle when the vehicle is reversing. Secondly, the information fusion algorithm using an adaptive Kalman filter is used to process the data obtained with the multi-sensor environmental perception module, which greatly improves the robustness of the sensors. Then the framework of a particle filter and low-rank representation is used to track the main obstacles. The low-rank representation is used to optimize an objective particle template that has the smallest L-1 norm. Finally, the electronic throttle opening and automatic braking is under control of the proposed vehicle reversing control strategy prior to any potential collisions, making the reversing control safer and more reliable. The final system simulation and practical testing results demonstrate the validity of the proposed multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety.

Sustainable Sea of Internet of Things: Wind Energy Harvesting System for Unmanned Surface Vehicles.

Harvesting wind energy from the environment and integrating it with the internet of things and artificial intelligence to enable intelligent ocean environment monitoring are effective approach. There are some challenges that limit the performance of wind energy harvesters, such as the larger start-up torque and the narrow operational wind speed range. To address these issues, this paper proposes a wind energy harvesting system with a self-regulation strategy based on piezoelectric and electromagnetic effects to achieve state monitoring for unmanned surface vehicles (USVs). The proposed energy harvesting system comprises eight rotation units with centrifugal adaptation and four piezoelectric units with a magnetic coupling mechanism, which can further reduce the start-up torque and expand the wind speed range. The dynamic model of the energy harvester with the centrifugal effect is explored, and the corresponding structural parameters are analyzed. The simulation and experimental results show that it can obtain a maximum average power of 23.25 mW at a wind speed of 8 m/s. Furthermore, three different magnet configurations are investigated, and the optimal configuration can effectively decrease the resistance torque by 91.25% compared with the traditional mode. A prototype is manufactured, and the test result shows that it can charge a 2200 µF supercapacitor to 6.2 V within 120 s, which indicates that it has a great potential to achieve the self-powered low-power sensors. Finally, a deep learning algorithm is applied to detect the stability of the operation, and the average accuracy reached 95.33%, which validates the feasibility of the state monitoring of USVs.

A self-powered and self-sensing knee negative energy harvester.

Wearable devices realize health monitoring, information transmission, etc. In this study, the human-friendliness, adaptability, reliability, and economy (HARE) principle for designing human energy harvesters is first proposed and then a biomechanical energy harvester (BMEH) is proposed to recover the knee negative energy to generate electricity. The proposed BMEH is mounted on the waist of the human body and connected to the ankles by ropes for driving. Double-rotor mechanism and half-wave rectification mechanism design effectively improves energy conversion efficiency with higher power output density for more stable power output. The experimental results demonstrate that the double-rotor mechanism increases the output power of the BMEH by 70% compared to the single magnet-rotor mechanism. And the output power density of BMEH reaches 0.07 W/kg at a speed of 7 km/h. Furthermore, the BMEH demonstrates the excitation mode detection accuracy of 99.8% based on the Gate Recurrent Unit deep learning model with optimal parameters.

Harvesting Inertial Energy and Powering Wearable Devices: A Review.

Amidst the swift progression of microelectronics and Internet of Things technology, wearable devices are gradually gaining ground in the domains of human health monitoring. Recently, human bioenergy harvesting has emerged as a plausible alternative to batteries. This paper delves into harvesting human inertial energy that stimulates inertial masses through human motion and then transmutes the motion of the inertial masses into electrical energy. The inertial energy harvester is better suited for low-frequency and irregular human motion. This review first identifies the sources of human motion excitation that are compatible with inertial energy harvesters and then provides a summary of the operating principles and the comparisons of the commonly used energy conversion mechanisms, including electromagnetic, piezoelectric, and triboelectric transducers. The review thoroughly summarizes the latest advancements in human inertial energy-harvesting technology that are categorized and grouped based on their excitation sources and mechanical modulation methods. In addition, the review outlines the applications of inertial energy harvesters in powering wearable devices, medical health monitoring, and as mobile power sources. Finally, the challenges faced by inertial energy-harvesting technologies are discussed, and the review provides a perspective on the potential developments in the field.

An energy harvesting shock absorber for powering on-board electrical equipment in freight trains.

To realize smart detection and safe operation of freight trains, a continuous and stable energy source is required for electrical equipment on the train. It is a feasible scheme to harvest the vibration energy of train suspension to supply power for on-board electrical equipment. This paper presents an energy-harvesting shock absorber (EHSA) based on the slider-crank mechanism and ratchet-pawl mechanism, which provide a vibration reduction effect and renewable electricity. To determine the damping performance and the power generation performance of EHSA, a dynamic model is established based on MATLAB. According to the tests on the mechanical testing and sensing (MTS) bench, the maximum power generation mechanical efficiency of the EHSA is 67.75%, and the maximum output power of the EHSA is 1.65W. In addition, the charging tests on the MTS bench show that the proposed device is applicable to power on-board electrical equipment on freight trains.

A negative-work knee energy harvester based on homo-phase transfer for wearable monitoring devices.

Wearable health monitoring devices can effectively capture human body information and are widely used in health monitoring, but battery life is an important bottleneck in its development. A full negative-work energy harvester based on the homo-phase transfer mechanism by analyzing human motion characteristics was proposed in this paper. The system was designed based on the homo-phase transfer mechanism, including a motion input module, gear acceleration module, energy conversion module, and electric energy storage module. The output performance in three human-level, downhill, and running states was tested, respectively. Finally, we have evaluated the feasibility of an energy harvester powering wearable health monitoring devices, and the harvester can generate 17.40 J/day power, which can satisfy the normal operation of a typical health monitoring device. This study has certain promoting significance for the development of a new generation of human health monitoring.

Techno-economic assessment of implementing photovoltaic water villas in Maldives.

Solar energy is considered to be an effective measure to alleviate the shortage of power supply in the Maldives. In this paper, a roof photovoltaic (PV) system integrated into water villas in the Maldives was investigated. Three islands-Ayada Maldives, Angaga Island Resort, and JA Manafaru, located in the southern, central, and northern parts of Maldives-were selected for a case study. The potential of PV installations in Ayada Maldives, Angaga Island Resort, and JA Manafaru reaches 1,410, 445, and 742 kW, with corresponding annual power generation of 2.04, 0.64, and 1.12 GWh, respectively. The profits over the life cycle of 25 years of the above three studied islands are 4.86, 1.52, and 2.90 million USD, respectively, with payback periods in the range of 6-7 years.

Small wind turbines and their potential for internet of things applications.

Wind energy is crucial for meeting climate and energy sustainability targets. Small wind turbines (SWTs) have gained significant attention due to their size and adaptability. These turbines have potential for Internet of Things (IoT) applications, particularly in powering large areas and low-power devices. This review examines SWTs for IoT applications, providing an extensive overview of their development, including wind energy rectifiers, power generation mechanisms, and IoT applications. The paper summarizes and compares different types of wind energy rectifiers, explores recent advancements and representative work, and discusses applicable generator systems such as electromagnetic, piezoelectric, and triboelectric nanogenerators. In addition, it thoroughly reviews the latest research on IoT application scenarios, including transportation, urban environments, intelligent agriculture, and self-powered wind sensing. Lastly, the paper identifies future research directions and emphasizes the potential of interdisciplinary technologies in driving SWT development.

A review of energy harvesting from regenerative shock absorber from 2000 to 2021: advancements, emerging applications, and technical challenges.

In the automotive and transportation sectors, technological advancements and innovations aim to reduce fuel consumption and CO2 emissions of vehicles. In vehicles, a significant portion of fuel energy is wasted in heat, vibrations, and frictional losses. The vibration energy from vehicle suspension systems is always wasted in heat and can be utilized for useful purposes. Many researchers have designed various regenerative shock absorbers (RSA) to transform vibration energy into electrical energy that can charge electric vehicles' batteries and power low-wattage devices. The present work focuses on an in-depth summary of rotary, hydraulic, and linear electromagnetic RSA. Also, the applications of regenerated energy and technical challenges are discussed. In RSA, the maximum energy harvesting, and ride comfort of the vehicle cannot be achieved simultaneously. The weight of RSA may increase due to the integration of some additional components compared with conventional shock absorbers. It is necessary to examine the impact of weight on the vehicle's road handling and ride comfort. The hydraulic RSAs have low energy harvesting efficiency, so they are not suitable for lightweight vehicles despite their higher energy harvestability than rotary and linear RSAs. The bibliometric analysis is conducted using the visualization of similarities (VOS) viewer to visualize the contributing authors and countries and specify the research themes. The articles are collected from the Web of Science using keywords related to energy harvesting from 2000 to 2021. Authors from China are more productive than others, with the highest number of publications related to the energy-harvesting from RSAs in 2019.

Parameter optimization analysis of rotary electromagnetic vibration energy harvester for performance enhancement under free vibration.

In this paper, three new important aspects of rotary electromagnetic vibration energy harvesting technology (RE-VEH) are concerned and investigated: (i) vibro-electric coupling mechanism of the RE-VEH system is studied through theoretical modeling; (ii) quantitative analysis of system parameters based on numerical simulation method is carried out for the optimal design of RE-VEH; and (iii) dynamic power output performance of the RE-VEH system in free vibration is discussed. The parameter adjusting methods of the RE-VEH system in free vibration mode are obtained through theoretical analysis and numerical simulation. The experimental results show that the power output performance of RE-VEH in free vibration mode matches the numerical simulation results. The simulation and experimental results show that the maximum voltage output and power output of the RE-VEH with different structure parameters under free vibration can be up to the level of 100∼101 V/watt. The above results indicate that RE-VEH in a free vibration environment has significant energy output performance.

A review of vibration energy harvesting in rail transportation field.

In this paper, we review, compare, and analyze previous studies on vibration energy harvesting and related technologies. First, the paper introduces the basic aspects of vibration energy acquisition in the railway environment, including vibration frequency, train speed, energy flow in the train, and vibration energy harvesting potential. Generally, the methods for scavenging vibration energy caused by passing trains can be divided into four categories: electromagnetic harvesters, piezoelectric harvesters, triboelectric harvesters, and hydraulic harvesters. The structure, output performance, merits, and disadvantages of different energy harvesting strategies are summarized and compared. The application of vibration energy harvesters is explained as supplying power to monitoring sensors on the line side and the vehicle side. Finally, the paper addresses the challenges and difficulties that have not been completely resolved in the current research literature, including system stability, durability, and economy. Some recommendations to fill these research gaps are put forward for further investigation.

A vibration energy harvester for freight train track self-powered application.

The safe and stable operation of the widely distributed freight trains urgently needs to solve the power supply problem of the freight train track monitoring network. In this article, an innovative and efficient energy harvesting mechanism is designed based on a mechanical vibration rectifier (MVR), with four modules of motion conversion, motion rectification, generator, and storage. The motion conversion module converts the linear vibration of the rail into rotational motion through the ball screw. The motion rectification module integrates mechanical rectification and transmission speed-up and converts the bidirectional rotation of the transmission shaft into a faster unidirectional rotation of the generator. Experiments show that the power and mechanical efficiency of the proposed vibration energy harvester can reach 28.0416W and 75.92%, respectively. This high-performance MVR can meet the power demand of wireless sensor networks by harvesting vibration energy to ensure the normal operation of freight trains.

A near-zero energy system based on a kinetic energy harvester for smart ranch.

Smart ranch relying on sensor systems to realize monitoring of animals and the environment has emerged with the promotion of the Internet of Things (IoT). This paper proposes a near-zero energy system (NZES) based on a kinetic energy harvester (KEH) for smart ranch. The KEH is based on motion enhancement mechanism (MEM) for kinetic energy recovery from animal movement to realize self-powered applications of smart ranch. The MEM realizes the input and enhancement of weak kinetic energy based on bistable inertial swing. The KEH is analyzed theoretically and experimentally based on cattle leg movement. Under weak excitation (low-frequency and amplitude swing), the maximum voltage growth rate of the KEH based on the MEM reaches 103.7% compared with the linear KEH. The results of application feasibility tests, dressing field experiments, and application outlook show that the KEH has the potential to realize self-powered applications in the NZES of smart ranch.

An extended-range wave-powered autonomous underwater vehicle applied to underwater wireless sensor networks.

The autonomous underwater vehicle has widespread applications in marine resource exploration, seabed search and rescue, underwater military reconnaissance, and marine environmental monitoring. Owing to the limited battery capacity, autonomous underwater vehicles usually only operate for several hours or days at a time. This article presents an extended-range wave-powered autonomous underwater vehicle (WPAUV) for underwater wireless sensor networks. Through theoretical analysis, simulation, dry and field experiments, the power generation performance of the extended-range WPAUV was evaluated. Under different wave amplitudes and wave frequencies, the mechanical efficiency of the extended-range WPAUV ranges from 20.41% to 81.56%. The average efficiency is 45.35%. In the field experiments, under calm ocean conditions, the maximum instantaneous power can reach 67.74W with an average of 10.18W. This high performance manifests that the extended-range WPAUV can effectively scavenge wave energy and converts it into electrical energy for expanding the cruising mileage.

Vehicle driver drowsiness detection method using wearable EEG based on convolution neural network.

Vehicle drivers driving cars under the situation of drowsiness can cause serious traffic accidents. In this paper, a vehicle driver drowsiness detection method using wearable electroencephalographic (EEG) based on convolution neural network (CNN) is proposed. The presented method consists of three parts: data collection using wearable EEG, vehicle driver drowsiness detection and the early warning strategy. Firstly, a wearable brain computer interface (BCI) is used to monitor and collect the EEG signals in the simulation environment of drowsy driving and awake driving. Secondly, the neural networks with Inception module and modified AlexNet module are trained to classify the EEG signals. Finally, the early warning strategy module will function and it will sound an alarm if the vehicle driver is judged as drowsy. The method was tested on driving EEG data from simulated drowsy driving. The results show that using neural network with Inception module reached 95.59% classification accuracy based on one second time window samples and using modified AlexNet module reached 94.68%. The simulation and test results demonstrate the feasibility of the proposed drowsiness detection method for vehicle driving safety.

Additively manufactured nano-mechanical energy harvesting systems: advancements, potential applications, challenges and future perspectives.

Additively manufactured nano-MEH systems are widely used to harvest energy from renewable and sustainable energy sources such as wind, ocean, sunlight, raindrops, and ambient vibrations. A comprehensive study focusing on in-depth technology evolution, applications, problems, and future trends of specifically 3D printed nano-MEH systems with an energy point of view is rarely conducted. Therefore, this paper looks into the state-of-the-art technologies, energy harvesting sources/methods, performance, implementations, emerging applications, potential challenges, and future perspectives of additively manufactured nano-mechanical energy harvesting (3DP-NMEH) systems. The prevailing challenges concerning renewable energy harvesting capacities, optimal energy scavenging, power management, material functionalization, sustainable prototyping strategies, new materials, commercialization, and hybridization are discussed. A novel solution is proposed for renewable energy generation and medicinal purposes based on the sustainable utilization of recyclable municipal and medical waste generated during the COVID-19 pandemic. Finally, recommendations for future research are presented concerning the cutting-edge issues hurdling the optimal exploitation of renewable energy resources through NMEHs. China and the USA are the most significant leading forces in enhancing 3DP-NMEH technology, with more than 75% contributions collectively. The reported output energy capacities of additively manufactured nano-MEH systems were 0.5-32 mW, 0.0002-45.6 mW, and 0.3-4.67 mW for electromagnetic, piezoelectric, and triboelectric nanogenerators, respectively. The optimal strategies and techniques to enhance these energy capacities are compiled in this paper.