Ultrasensitive wearable sensor with novel hybrid structures of silver nanowires and carbon nanotubes in fluoroelastomer: Multi-directional sensing for human health monitoring and stretchable electronics

2022 ◽  
Vol 26 ◽  
pp. 101295
Author(s):  
Shaghayegh Shajari ◽  
Shashank Ramakrishnan ◽  
Kunal Karan ◽  
Les Jozef Sudak ◽  
Uttandaraman Sundararaj
Nano Research ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 919-926 ◽  
Author(s):  
Yanjing Zhang ◽  
Pei He ◽  
Meng Luo ◽  
Xiaowen Xu ◽  
Guozhang Dai ◽  
...  

2021 ◽  
Author(s):  
Jeng-Hun Lee ◽  
Haomin Chen ◽  
Eunyoung Kim ◽  
Heng Zhang ◽  
Ke Wu ◽  
...  

Continuous real-time measurement of body temperature using a wearable sensor is an essential part of human health monitoring. Electrospun aligned carbon nanofiber (ACNF) films are employed to assemble flexible temperature...


Author(s):  
Jiyuan Gao ◽  
Kezheng Shang ◽  
Yichun Ding ◽  
Zhenhai Wen

Flexible and wearable sensors have shown great potential in tremendous applications such as human health monitoring, smart robots, and human–machine interfaces, yet the lack of suitable flexible power supply devices...


2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Yunlong Xie ◽  
Huanhuan Qian ◽  
Yijun Zhong ◽  
Hangming Guo ◽  
Yong Hu

We demonstrate a facile and novel chemical precipitation strategy for the accurate coating of TiO2nanoparticles on the surface of carbon nanotubes (CNTs) to form CNT/TiO2nanohybrids, which only requires titanium sulfate and CNTs as starting materials and reacts in the alkaline solution at 60°C for 6 h. Using this process, the as-prepared hybrid structures preserved the good dispersity and uniformity of initial CNTs. Furthermore, the CNT/TiO2nanohybrids show a broad blue luminescence at 469 nm and exhibit significantly enhanced photocatalytic activity for the degradation of rhodamine B (RhB) under visible-light irradiation, which is about 1.5 times greater than that of commercial Degussa P25 TiO2nanoparticles. It is believed that this facile chemical precipitation strategy is scalable and its application can be extended to synthesize other CNT/oxide nanohybrids for various applications.


2021 ◽  
Author(s):  
Liangye Li ◽  
Changying Song ◽  
Yunfei Liu ◽  
Shunfeng Sheng ◽  
Zhijun Yan ◽  
...  

Soft Matter ◽  
2017 ◽  
Vol 13 (37) ◽  
pp. 6390-6395 ◽  
Author(s):  
Ye Rim Lee ◽  
Hyungho Kwon ◽  
Do Hoon Lee ◽  
Byung Yang Lee

Electrodes consisting of silver nanowires and carbon nanotubes enable a dielectric elastomer actuator to become highly stretchable and optically transparent.


2020 ◽  
Vol 10 (20) ◽  
pp. 7122
Author(s):  
Ahmad Jalal ◽  
Mouazma Batool ◽  
Kibum Kim

The classification of human activity is becoming one of the most important areas of human health monitoring and physical fitness. With the use of physical activity recognition applications, people suffering from various diseases can be efficiently monitored and medical treatment can be administered in a timely fashion. These applications could improve remote services for health care monitoring and delivery. However, the fixed health monitoring devices provided in hospitals limits the subjects’ movement. In particular, our work reports on wearable sensors that provide remote monitoring that periodically checks human health through different postures and activities to give people timely and effective treatment. In this paper, we propose a novel human activity recognition (HAR) system with multiple combined features to monitor human physical movements from continuous sequences via tri-axial inertial sensors. The proposed HAR system filters 1D signals using a notch filter that examines the lower/upper cutoff frequencies to calculate the optimal wearable sensor data. Then, it calculates multiple combined features, i.e., statistical features, Mel Frequency Cepstral Coefficients, and Gaussian Mixture Model features. For the classification and recognition engine, a Decision Tree classifier optimized by the Binary Grey Wolf Optimization algorithm is proposed. The proposed system is applied and tested on three challenging benchmark datasets to assess the feasibility of the model. The experimental results show that our proposed system attained an exceptional level of performance compared to conventional solutions. We achieved accuracy rates of 88.25%, 93.95%, and 96.83% over MOTIONSENSE, MHEALTH, and the proposed self-annotated IM-AccGyro human-machine dataset, respectively.


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