scholarly journals A Flexible and Stretchable Self-Powered Nanogenerator in Basketball Passing Technology Monitoring

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2584
Author(s):  
Changjun Jia ◽  
Yongsheng Zhu ◽  
Fengxin Sun ◽  
Tianming Zhao ◽  
Rongda Xing ◽  
...  
Keyword(s):  
Real Time ◽  
Inorganic Salt ◽  
Rapid Development ◽  
Human Motion ◽  
Small Scale ◽  
New Approach ◽  
Motion Data ◽  
Fifth Generation ◽  
Self Powered ◽  
Generation Technology

The rapid development of the fifth generation technology poses more challenges in the human motion inspection field. In this study, a nanogenerator, made by PVDF, ionic hydrogel, and PDMS, is used. Furthermore, a transparent, stretchable, and biocompatible PENG (TSB-PENG) is presented, which can be used as a self-powered sensor attached to the athlete’s joints, which helps to monitor the training and improve the subject’s performance. This device shows the ability to maintain a relatively stable output, under various external environments (e.g., inorganic salt, organic matter and temperature). Additionally, TSB-PENG can supply power to small-scale electronic equipment, such as Bluetooth transmitting motion data in real time. This study can provide a new approach to designing lossless, real-time, portable, and durable self-powered sensors in the sports motoring field.

scholarly journals Light-Weight, Self-Powered Sensor Based on Triboelectric Nanogenerator for Big Data Analytics in Sports

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2322
Author(s):  
Xiaofei Ma ◽  
Xuan Liu ◽  
Xinxing Li ◽  
Yunfei Ma
Keyword(s):  
Big Data ◽  
Real Time ◽  
Data Analytics ◽  
Mechanical Energy ◽  
Rapid Development ◽  
Big Data Analytics ◽  
Triboelectric Nanogenerator ◽  
Light Weight ◽  
Table Tennis ◽  
Self Powered

With the rapid development of the Internet of Things (IoTs), big data analytics has been widely used in the sport field. In this paper, a light-weight, self-powered sensor based on a triboelectric nanogenerator for big data analytics in sports has been demonstrated. The weight of each sensing unit is ~0.4 g. The friction material consists of polyaniline (PANI) and polytetrafluoroethylene (PTFE). Based on the triboelectric nanogenerator (TENG), the device can convert small amounts of mechanical energy into the electrical signal, which contains information about the hitting position and hitting velocity of table tennis balls. By collecting data from daily table tennis training in real time, the personalized training program can be adjusted. A practical application has been exhibited for collecting table tennis information in real time and, according to these data, coaches can develop personalized training for an amateur to enhance the ability of hand control, which can improve their table tennis skills. This work opens up a new direction in intelligent athletic facilities and big data analytics.

scholarly journals CSI-HC: A WiFi-Based Indoor Complex Human Motion Recognition Method

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Zhanjun Hao ◽  
Yu Duan ◽  
Xiaochao Dang ◽  
Tong Zhang
Keyword(s):  
Real Time ◽  
Human Motion ◽  
Detection Accuracy ◽  
Behavior Recognition ◽  
Motion Recognition ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Motion Data ◽  
Indoor Scenes ◽  
Human Motion Recognition

WiFi indoor personnel behavior recognition has become the core technology of wireless network perception. However, the existing human behavior recognition methods have great challenges in terms of detection accuracy, intrusion, and complexity of operations. In this paper, we firstly analyze and summarize the existing human motion recognition schemes, and due to the existence of the problems in them, we propose a noninvasive, highly robust complex human motion recognition scheme based on Channel State Information (CSI), that is, CSI-HC, and the traditional Chinese martial art XingYiQuan is verified as a complex motion background. CSI-HC is divided into two phases: offline and online. In the offline phase, the human motion data are collected on the commercial Atheros NIC and a powerful denoising method is constructed by using the Butterworth low-pass filter and wavelet function to filter the outliers in the motion data. Then, through Restricted Boltzmann Machine (RBM) training and classification, we establish offline fingerprint information. In the online phase, SoftMax regression is used to correct the RBM classification to process the motion data collected in real time and the processed real-time data are matched with the offline fingerprint information. On this basis, the recognition of a complex human motion is realized. Finally, through repeated experiments in three classical indoor scenes, the parameter setting and user diversity affecting the accuracy of motion recognition are analyzed and the robustness of CSI-HC is detected. In addition, the performance of the proposed method is compared with that of the existing motion recognition methods. The experimental results show that the average motion recognition rate of CSI-HC in three classic indoor scenes reaches 85.4%, in terms of motion complexity and indoor recognition accuracy. Compared with other algorithms, it has higher stability and robustness.

Real time evaluation algorithm of human motion in tennis training robot

2020 ◽  
pp. 1-9
Author(s):  
Yingying Wang ◽  
Yongzhi Zhang
Keyword(s):  
Real Time ◽  
Rapid Development ◽  
Human Movement ◽  
Human Motion ◽  
Sports Activity ◽  
Training Requirements ◽  
Core Theory ◽  
Evaluation Algorithm ◽  
Stability And Accuracy ◽  
Time Evaluation

Tennis is a set of sports and entertainment and a sports activity, since 2014, tennis in China has been another rapid development. With the development of economy and technology, tennis training mode has been further optimized and reformed. At present, tennis training robot is the mainstream way to train athletes. However, there are some defects in the current tennis training robots, such as the low accuracy of human motion real-time evaluation, and the lack of stability. Therefore, this paper puts forward the related research on the real-time evaluation algorithm of human motion in tennis training robots, hoping to make up for the deficiency in this field. The research of this paper is mainly divided into four parts. The first part is to analyze the current situation of technology research in this field and put forward the idea of this paper by analyzing the shortcomings of the existing technology. The second part is the related basic theory research; this part deeply studies the core theory of tennis training and intelligent training robot, which provides a theoretical basis for the realization of the optimization scheme. The third part is the design and implementation of a real-time human motion evaluation optimization algorithm for tennis training robots. At the end of the paper, that is, the fourth part, through the way of field test and investigation, further proves the superiority of the improved real-time evaluation algorithm of human movement. The algorithm has good stability and accuracy and can meet the existing tennis training requirements.

Load-Tolerant, High-Efficiency Self-Powered Energy Harvesting Scheme Using a Nonlinear Approach

2014 ◽  
Vol 1 (3-4) ◽  
Author(s):  
Mickaël Lallart ◽  
Claude Richard ◽  
Yang Li ◽  
Yi-Chieh Wu ◽  
Daniel Guyomar
Keyword(s):  
Energy Harvesting ◽  
High Efficiency ◽  
Piezoelectric Materials ◽  
Small Scale ◽  
Energy Scavenging ◽  
Resistive Load ◽  
Vibration Energy Harvesting ◽  
New Approach ◽  
Nonlinear Approach ◽  
Self Powered

AbstractSmall-scale energy harvesting has become a particularly hot topic for replacing batteries in autonomous or nomad systems. In particular, vibration energy harvesting using piezoelectric elements has experienced a significant amount of research over the last decade as vibrations are widely available in many environments and as piezoelectric materials can be easily embedded. However, the energy scavenging abilities of such systems are still limited and are very sensitive to the connected load. The purpose of this paper is to expose a new approach based on synchronous switching on resistive load, which allows both a significant enhancement of the energy harvesting capabilities as well as a high tolerance to a change of the impedance of the connected system, especially in the low value region. It is theoretically and experimentally shown that such an approach permits increasing the energy harvesting abilities by a factor 4 compared to classical DC energy harvesting approach. Furthermore, the self-powering possibility and automatic load adaptation of the proposed method is experimentally discussed, showing the realistic viability of the technique.

scholarly journals Self-Powered Smart Insole for Monitoring Human Gait Signals

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5336 ◽  
Author(s):  
Wei Wang ◽  
Junyi Cao ◽  
Jian Yu ◽  
Rong Liu ◽  
Chris R. Bowen ◽  
...  
Keyword(s):  
Vinylidene Fluoride ◽  
Rapid Development ◽  
Human Motion ◽  
Multiscale Entropy ◽  
Human Gait ◽  
Pvdf Film ◽  
Aluminum Electrode ◽  
Wearable Electronics ◽  
Motion Speed ◽  
Self Powered

With the rapid development of low-power consumption wireless sensors and wearable electronics, harvesting energy from human motion to enable self-powered sensing is becoming desirable. Herein, a pair of smart insoles integrated with piezoelectric poly(vinylidene fluoride) (PVDF) nanogenerators (NGs) are fabricated to simultaneously harvest energy from human motion and monitor human gait signals. Multi-target magnetron sputtering technology is applied to form the aluminum electrode layers on the surface of the PVDF film and the self-powered insoles are fabricated through advanced 3D seamless flat-bed knitting technology. Output responses of the NGs are measured at different motion speeds and a maximum value of 41 V is obtained, corresponding to an output power of 168.1 μW. By connecting one NG with an external circuit, the influence of external resistance, capacitor, and motion speed on the charging characteristics of the system is systematically investigated. To demonstrate the potential of the smart insoles for monitoring human gait signals, two subjects were asked to walk on a treadmill at different speeds or with a limp. The results show that one can clearly distinguish walking with a limp from regular slow, normal, and fast walking states by using multiscale entropy analysis of the stride intervals.

Motion Prediction with Artificial Neural Networks Using Wearable Strain Sensors Based on Flexible Thin Graphite Films

2019 ◽  
Vol 826 ◽  
pp. 111-116
Author(s):  
Takahiro Kanokoda ◽  
Yuki Kushitani ◽  
Moe Shimada ◽  
Jun Ichi Shirakashi
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Wearable Devices ◽  
Human Motion ◽  
Strain Sensors ◽  
Motion Prediction ◽  
Prediction System ◽  
Hand Motion ◽  
Motion Data ◽  
Human Motion Prediction

A human motion prediction system can be used to estimate human gestures in advance to the actual action for reducing delays in interactive system. We have already reported a method of simple and easy fabrication of strain sensors and wearable devices using pyrolytic graphite sheets (PGSs). The wearable electronics could detect various types of human motion, with high durability and fast response. In this study, we have demonstrated hand motion prediction by neural networks (NNs) using hand motion data obtained from data gloves based on PGSs. In our experiments, we measured hand motions of subjects for learning. We created 4-layered NNs to predict human hand motion in real-time. As a result, the proposed system successfully predicted hand motion in real-time. Therefore, these results suggested that human motion prediction system using NNs is able to forecast various types of human behavior using human motion data obtained from wearable devices based on PGSs.

scholarly journals Special Issue on Energy Harvesters and Self-Powered Sensors for Smart Electronics

Micromachines ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1455
Author(s):  
Qiongfeng Shi ◽  
Huicong Liu
Keyword(s):  
Internet Of Things ◽  
Wireless Network ◽  
Rapid Development ◽  
The Internet ◽  
Special Issue ◽  
Energy Harvesters ◽  
Fifth Generation ◽  
Self Powered ◽  
The Internet Of Things ◽  
Smart Electronics

In recent years, we have witnessed the revolutionary innovation and flourishing advancement of the Internet of things (IoT), which will maintain a strong momentum even more with the gradual rollout of the fifth generation (5G) wireless network and the rapid development of personal healthcare electronics [...]

scholarly journals TO THE PROBLEM OF APPLYING WI-FI ACCESS POINTS FOR REGISTERING MOTION AT THE SITE IN THE ABSENCE OF A WI-FI MODULE

2019 ◽  
pp. 73-78 ◽  
Author(s):  
Ivan Leonidovich Reva ◽  
Alexey Aleksandrovich Bogdanov ◽  
Ekaterina Andreevna Malakhova
Keyword(s):  
Global Positioning System ◽  
Rapid Development ◽  
Human Movement ◽  
Human Motion ◽  
Radio Network ◽  
New Approach ◽  
Unauthorized Access ◽  
Single Person ◽  
Global Positioning ◽  
Access Control System

The article describes the problem of registration of human movement on the object and protection of the object against unauthorized access. Global Positioning System which is well proven in open field has low precision within the premises. Due to the rapid development of Wi-Fi technology and the need to organize monitoring of human motion in the protected premises, there is being developed a new approach to registering a person on site using Wi-Fi. The problem of registering unauthorized access to the object by means of Wi-Fi radio network has been considered, its strengths and weaknesses have been studied. Most organizations actively use corporative and public Wi-Fi networks and beneficially apply this well-developed infrastructure for detecting the human presence in the premises and determining their position. It has been stated that using Wi-Fi network is more profitable than installing a special access control system. The aim of the research is to develop a human motion registering system at the site protected without using a Wi-Fi-module. There have been presented experiment results of registering human motion by means of the well-developed Wi-Fi infrastructure, the experiments being conducted to analyze changes of a signal level at different positions of a single person or a number of persons in the premises. It has been inferred that the level of Wi-Fi signal changes when a person or a group of persons are present in the room, even if they don’t have a Wi-Fi-module; this fact helps register the human motion in the protected premises.

scholarly journals Real-time Rendering of Small-scale Volumetric Structure on Animated Surfaces

2021 ◽  
Author(s):  
Artemiy Leshonkov ◽  
Vladimir Alexandrovich Frolov
Keyword(s):  
Real Time ◽  
Mapping Technique ◽  
Small Scale ◽  
Base Surface ◽  
New Approach ◽  
Levels Of Detail ◽  
Space Geometry ◽  
Mapping Techniques ◽  
Shell Geometry

There are a lot of methods for rendering of shell-space geometry, represented through voxel texture, known for today. While the topic is well studied in terms of techniques for applying this geometry onto surfaces, a little attention was paid to representation of sub-pixel details of the geometry. Such details are prone to produce aliasing artifacts and reduce performance due to bad cache utilization. In this paper we solve these problems by introducing levels of detail for voxel textures within shell mapping technique. The main problem here is that less detailed levels begin to contain semi-transparent voxels on the edge of an encoded surface, which requires additional handling. For this we present a new approach for order independent transparency rendering based on depth peeling. We extend the algorithm by adding additional resolving pass which allows to fully utilize hardware z-buffering to reduce amount of overdraw. This significantly reduces cost of each subsequent peeling pass. Empirically, 3-4 of such passes is enough to produce good quality results in most cases. Another issue with shell mapping techniques is that shell geometry is constructed offline, making base surface to be static. By slightly modifying the method, we made the construction to be performed on-the-fly on GPU and be applicable for animated surfaces.

Intelligent recognition of dance training movements based on machine learning and embedded system

2021 ◽  
pp. 1-13
Author(s):  
Dixin Zhang
Keyword(s):  
Real Time ◽  
Smart Home ◽  
Three Dimensional ◽  
Recognition Algorithm ◽  
Human Motion ◽  
Support Vector ◽  
Motion Recognition ◽  
Motion Data ◽  
Local Database ◽  
Error Recognition

Recognizing human movement is an important research topic in the field of human-computer interaction, and people expect it to be used in smart homes, virtual reality, and electronic games. Based on the interaction between humans and computers, more and more attention has been paid, especially in the field of smart home action recognition. Through observation, people can understand the intention of intelligent interaction is included in the main part. However, the current recognition algorithms still cannot meet the actual requirements of the accuracy, real-time and robustness of human motion recognition. Especially in order to recognize complex human movements in real time, it is imperative to solve several problems in motion capture and recognition. Establishing the feature parameter angle of the feature vector space of motion data, using the pre-recognition algorithm is based on multi-class support vector machines. The motion recognition algorithm takes advantage of the accurate and fast classification function of svm. Based on the structural differences of the motion data, most of the data can be correctly identified. The optimal motion recognition algorithm uses hmm to correct the svm error recognition result through the random constraint relationship between the error recognition data and the actual label. Based on data simulation and analysis, each variable determined by the grid search algorithm has the highest accuracy in the optimization of each variable of the support vector machine. Finally, a smart home simulation experiment interactive system was built, and a local database was created, including 1,300 processes. The real-time algorithm uses the data in the local database for training and testing. Experimental results show that the motion recognition algorithm in this paper improves the accuracy and robustness of complex motion recognition. While meeting the real-time recognition conditions, the correct answer rate of the final operation can reach 9.6%. The human motion trajectory recognition system uses the three-dimensional trajectory of gestures to recognize motion. The information in the three-dimensional space is more comprehensive, and the orbit recognition is more robust.

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