Highly Sensitive Flexible Modulus Sensor for Softness Perception and Clinical Application

2022 ◽  
Author(s):  
Qiang Zou ◽  
Fengrui Yang ◽  
Yaodong Wang
Keyword(s):  
Wearable Sensors ◽  
Medical Application ◽  
Human Machine Interaction ◽  
Highly Sensitive ◽  
New Strategy ◽  
Modulus Measurement ◽  
Machine Interaction ◽  
Intrinsic Function ◽  
Capacitance Response ◽  
Fast Dynamic

Abstract The wearable sensors for softness measuring are emerging as a solution of softness perception, which is an intrinsic function of human skin, for electronic skin and human-machine interaction. However, these wearable sensors suffer from a key challenge: the modulus of an object can not be characterized directly, which originates from the complicated transduction mechanism. To address this key challenge, we developed a flexible and wearable modulus sensor that can simultaneously measure the pressure and modulus without mutual interference. The modulus sensing was realized by merging the electrostatic capacitance response from the pressure sensor and the ionic capacitance response from the indentation sensor. Via the optimized structure, our sensor exhibits high modulus sensitivity of 1.9 × 102 in 0.06 MPa, a fast dynamic response time of 100 ms, and high mechanical robustness for over 2500 cycles. We also integrated the sensor onto a prosthetic hand and surgical probe to demonstrate its capability for pressure and modulus sensing. This work provides a new strategy for modulus measurement, which has great potential in softness sensing and medical application.

Self-healing, Mechanically Robust, and 3D Printable Ionogel for Highly Sensitive and Long-Term Reliable Ionotronics

2022 ◽  
Author(s):  
Manwen Zhang ◽  
Xinglin Tao ◽  
Ran Yu ◽  
Yangyang He ◽  
Xinpan Li ◽  
...  
Keyword(s):  
Self Healing ◽  
Human Machine Interaction ◽  
Flexible Sensors ◽  
Electrical Signals ◽  
Highly Sensitive ◽  
Machine Interaction

Flexible sensors which can transduce various stimuli (e.g., strain, pressure, temperature) into electrical signals are highly in demand due to the development of human-machine interaction. However, it is still a...

Highly Sensitive Strain-sensor Based on Silica@Polyaniline Core-Shell Particle Reinforced Hydrogel with Excellent Flexibility, Stretchability, Toughness and Conductivity

Soft Matter ◽  
2020 ◽  
Author(s):  
Youqiang Li ◽  
Chuang Liu ◽  
Xue Lv ◽  
shulin sun
Keyword(s):  
Health Monitoring ◽  
Strain Sensor ◽  
Strain Sensors ◽  
Personal Health ◽  
Core Shell ◽  
Human Machine Interaction ◽  
Highly Sensitive ◽  
Personal Health Monitoring ◽  
Conductive Hydrogels ◽  
Machine Interaction

Hydrogel-based flexible strain sensors for personal health monitoring and human-machine interaction have attracted wide interest of researchers. In this paper, hydrophobic association and nanocomposite conductive hydrogels were successfully prepared by...

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.

Wearable Sensors‐Enabled Human–Machine Interaction Systems: From Design to Application

2020 ◽  
pp. 2008936
Author(s):  
Ruiyang Yin ◽  
Depeng Wang ◽  
Shufang Zhao ◽  
Zheng Lou ◽  
Guozhen Shen
Keyword(s):  
Wearable Sensors ◽  
Human Machine Interaction ◽  
Machine Interaction

Conductive graphene-based E-textile for highly sensitive, breathable, and water-resistant multimodal gesture-distinguishable sensors

2020 ◽  
Vol 8 (29) ◽  
pp. 14778-14787
Author(s):  
Xurui Hu ◽  
Tao Huang ◽  
Zhiduo Liu ◽  
Gang Wang ◽  
Da Chen ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Control System ◽  
Remote Control ◽  
Pressure Sensors ◽  
Human Machine Interaction ◽  
Remote Control System ◽  
Highly Sensitive ◽  
Machine Interaction

Graphene E-textile exhibits excellent electrical conductivity, breathability, and washability. The application of a graphene E-textile on a wearable remote-control system by sewing the pressure sensors into the five fingers of a glove to invoke a human–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.

scholarly journals A Bibliometric Analysis of Human-Machine Interaction Methodology for Electric-Powered Wheelchairs Driving from 1998 to 2020

2021 ◽  
Vol 18 (14) ◽  
pp. 7567
Author(s):  
Xiaochen Zhang ◽  
Lanxin Hui ◽  
Linchao Wei ◽  
Fuchuan Song ◽  
Fei Hu
Keyword(s):  
The Elderly ◽  
Research Field ◽  
Future Research ◽  
Human Machine Interaction ◽  
Research Directions ◽  
The Past ◽  
Power Wheelchairs ◽  
The Disabled ◽  
Future Research Directions ◽  
Machine Interaction

Electric power wheelchairs (EPWs) enhance the mobility capability of the elderly and the disabled, while the human-machine interaction (HMI) determines how well the human intention will be precisely delivered and how human-machine system cooperation will be efficiently conducted. A bibliometric quantitative analysis of 1154 publications related to this research field, published between 1998 and 2020, was conducted. We identified the development status, contributors, hot topics, and potential future research directions of this field. We believe that the combination of intelligence and humanization of an EPW HMI system based on human-machine collaboration is an emerging trend in EPW HMI methodology research. Particular attention should be paid to evaluating the applicability and benefits of the EPW HMI methodology for the users, as well as how much it contributes to society. This study offers researchers a comprehensive understanding of EPW HMI studies in the past 22 years and latest trends from the evolutionary footprints and forward-thinking insights regarding future research.

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