Anti-freezing Organohydrogel Triboelectric Nanogenerator toward Highly Efficient and Flexible Human-machine Interaction at -30 °C

Nano Energy ◽  
2021 ◽  
pp. 106614
Author(s):  
Zhenyu Xu ◽  
Fenghua Zhou ◽  
Huizhen Yan ◽  
Guorong Gao ◽  
Huijing Li ◽  
...  
Keyword(s):  
Triboelectric Nanogenerator ◽  
Human Machine Interaction ◽  
Highly Efficient ◽  
Machine Interaction

scholarly journals A Robust and Wearable Triboelectric Tactile Patch as Intelligent Human-Achine Interface

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6366
Author(s):  
Zhiyuan Hu ◽  
Junpeng Wang ◽  
Yan Wang ◽  
Chuan Wang ◽  
Yawei Wang ◽  
...  
Keyword(s):  
Information Exchange ◽  
Triboelectric Nanogenerator ◽  
Wearable Electronics ◽  
Human Machine Interaction ◽  
Stable Performance ◽  
Machine Interface ◽  
Self Powered ◽  
Machine Interaction ◽  
Stable Signal ◽  
Processing Circuit

The human–machine interface plays an important role in the diversified interactions between humans and machines, especially by swaping information exchange between human and machine operations. Considering the high wearable compatibility and self-powered capability, triboelectric-based interfaces have attracted increasing attention. Herein, this work developed a minimalist and stable interacting patch with the function of sensing and robot controlling based on triboelectric nanogenerator. This robust and wearable patch is composed of several flexible materials, namely polytetrafluoroethylene (PTFE), nylon, hydrogels electrode, and silicone rubber substrate. A signal-processing circuit was used in this patch to convert the sensor signal into a more stable signal (the deviation within 0.1 V), which provides a more effective method for sensing and robot control in a wireless way. Thus, the device can be used to control the movement of robots in real-time and exhibits a good stable performance. A specific algorithm was used in this patch to convert the 1D serial number into a 2D coordinate system, so that the click of the finger can be converted into a sliding track, so as to achieve the trajectory generation of a robot in a wireless way. It is believed that the device-based human–machine interaction with minimalist design has great potential in applications for contact perception, 2D control, robotics, and wearable electronics.

Breath-based human–machine interaction system using triboelectric nanogenerator

Nano Energy ◽  
2019 ◽  
Vol 64 ◽  
pp. 103953 ◽  
Author(s):  
Baosen Zhang ◽  
Yingjie Tang ◽  
Ranran Dai ◽  
Hongyi Wang ◽  
Xiupeng Sun ◽  
...  
Keyword(s):  
Triboelectric Nanogenerator ◽  
Human Machine Interaction ◽  
Interaction System ◽  
Machine Interaction

scholarly journals Magnetic Array Assisted Triboelectric Nanogenerator Sensor for Real-Time Gesture Interaction

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Ken Qin ◽  
Chen Chen ◽  
Xianjie Pu ◽  
Qian Tang ◽  
Wencong He ◽  
...  
Keyword(s):  
Real Time ◽  
Signal Amplitude ◽  
Human Hand ◽  
Triboelectric Nanogenerator ◽  
Robotic Hand ◽  
Robotic Hands ◽  
Human Machine Interaction ◽  
Gesture Interaction ◽  
New Strategy ◽  
Machine Interaction

AbstractIn human-machine interaction, robotic hands are useful in many scenarios. To operate robotic hands via gestures instead of handles will greatly improve the convenience and intuition of human-machine interaction. Here, we present a magnetic array assisted sliding triboelectric sensor for achieving a real-time gesture interaction between a human hand and robotic hand. With a finger’s traction movement of flexion or extension, the sensor can induce positive/negative pulse signals. Through counting the pulses in unit time, the degree, speed, and direction of finger motion can be judged in real-time. The magnetic array plays an important role in generating the quantifiable pulses. The designed two parts of magnetic array can transform sliding motion into contact-separation and constrain the sliding pathway, respectively, thus improve the durability, low speed signal amplitude, and stability of the system. This direct quantization approach and optimization of wearable gesture sensor provide a new strategy for achieving a natural, intuitive, and real-time human-robotic interaction.

scholarly journals Self-Powered Intelligent Human-Machine Interaction for Handwriting Recognition

Research ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hang Guo ◽  
Ji Wan ◽  
Haobin Wang ◽  
Hanxiang Wu ◽  
Chen Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Human Computer Interaction ◽  
Supervised Machine Learning ◽  
Triboelectric Nanogenerator ◽  
Human Machine Interaction ◽  
The Neural Network ◽  
Interaction Interface ◽  
Machine Interaction ◽  
Computer Interaction

Handwritten signatures widely exist in our daily lives. The main challenge of signal recognition on handwriting is in the development of approaches to obtain information effectively. External mechanical signals can be easily detected by triboelectric nanogenerators which can provide immediate opportunities for building new types of active sensors capable of recording handwritten signals. In this work, we report an intelligent human-machine interaction interface based on a triboelectric nanogenerator. Using the horizontal-vertical symmetrical electrode array, the handwritten triboelectric signal can be recorded without external energy supply. Combined with supervised machine learning methods, it can successfully recognize handwritten English letters, Chinese characters, and Arabic numerals. The principal component analysis algorithm preprocesses the triboelectric signal data to reduce the complexity of the neural network in the machine learning process. Further, it can realize the anticounterfeiting recognition of writing habits by controlling the samples input to the neural network. The results show that the intelligent human-computer interaction interface has broad application prospects in signature security and human-computer interaction.

scholarly journals All-in-One Self-Powered Human–Machine Interaction System for Wireless Remote Telemetry and Control of Intelligent Cars

Nanomaterials ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2711
Author(s):  
Tingting Zhang ◽  
Lingjie Xie ◽  
Junyan Li ◽  
Zheguan Huang ◽  
Hao Lei ◽  
...  
Keyword(s):  
Intelligent Control ◽  
Mechanical Energy ◽  
Triboelectric Nanogenerator ◽  
Stable Operation ◽  
Wearable Electronics ◽  
Human Machine Interaction ◽  
System A ◽  
Self Powered ◽  
And Control ◽  
Machine Interaction

The components in traditional human–machine interaction (HMI) systems are relatively independent, distributed and low-integrated, and the wearing experience is poor when the system adopts wearable electronics for intelligent control. The continuous and stable operation of every part always poses challenges for energy supply. In this work, a triboelectric technology-based all-in-one self-powered HMI system for wireless remote telemetry and the control of intelligent cars is proposed. The dual-network crosslinking hydrogel was synthesized and wrapped with functional layers to fabricate a stretchable fibrous triboelectric nanogenerator (SF-TENG) and a supercapacitor (SF-SC), respectively. A self-charging power unit containing woven SF-TENGs, SF-SCs, and a power management circuit was exploited to harvest mechanical energy from the human body and provided power for the whole system. A smart glove designed with five SF-TENGs on the dorsum of five fingers acts as a gesture sensor to generate signal permutations. The signals were processed by the microcontroller and then wirelessly transmitted to the intelligent car for remote telemetry and control. This work is of paramount potential for the application of various terminal devices in self-powered HMI systems with high integration for wearable electronics.

A Study for Improving Device-Directed Speech Detection Toward Frictionless Human-Machine Interaction

2019 ◽  
Author(s):  
Che-Wei Huang ◽  
Roland Maas ◽  
Sri Harish Mallidi ◽  
Björn Hoffmeister
Keyword(s):  
Human Machine Interaction ◽  
Speech Detection ◽  
Machine Interaction

Concise CNN model for face expression recognition

2021 ◽  
pp. 1-9
Author(s):  
Harshadkumar B. Prajapati ◽  
Ankit S. Vyas ◽  
Vipul K. Dabhi
Keyword(s):  
Network Architecture ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Face Expression ◽  
Computational Overhead ◽  
Average Accuracy ◽  
Proposed Model ◽  
Face Expression Recognition ◽  
Expression Classification ◽  
Machine Interaction

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

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