Contact lens with integrated sensors for the next generation of human health monitoring

2011 ◽  
Author(s):  
Huanfen Yao ◽  
Ilkka Lahdesmaki ◽  
Babak A. Parviz
Keyword(s):  
Human Health ◽  
Health Monitoring ◽  
Contact Lens ◽  
Next Generation ◽  
Integrated Sensors

Material and Configuration Design Strategies towards Flexible and Wearable Power Supply Devices: A Review

2021 ◽  
Author(s):  
Jiyuan Gao ◽  
Kezheng Shang ◽  
Yichun Ding ◽  
Zhenhai Wen
Keyword(s):  
Human Health ◽  
Health Monitoring ◽  
Power Supply ◽  
Wearable Sensors ◽  
Configuration Design ◽  
Design Strategies ◽  
Human Machine Interfaces

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...

scholarly journals Addressing Human Variability in Next-Generation Human Health Risk Assessments of Environmental Chemicals

2013 ◽  
Vol 121 (1) ◽  
pp. 23-31 ◽  
Author(s):  
Lauren Zeise ◽  
Frederic Y. Bois ◽  
Weihsueh A. Chiu ◽  
Dale Hattis ◽  
Ivan Rusyn ◽  
...  
Keyword(s):  
Health Risk ◽  
Human Health ◽  
Human Health Risk ◽  
Environmental Chemicals ◽  
Risk Assessments ◽  
Next Generation ◽  
Health Risk Assessments

Wearable and Alignment-free Optical Microfiber Device for Human Health Monitoring

2021 ◽  
Author(s):  
Liangye Li ◽  
Changying Song ◽  
Yunfei Liu ◽  
Shunfeng Sheng ◽  
Zhijun Yan ◽  
...  
Keyword(s):  
Human Health ◽  
Health Monitoring ◽  
Alignment Free ◽  
Optical Microfiber

scholarly journals Stochastic Recognition of Physical Activity and Healthcare Using Tri-Axial Inertial Wearable Sensors

2020 ◽  
Vol 10 (20) ◽  
pp. 7122
Author(s):  
Ahmad Jalal ◽  
Mouazma Batool ◽  
Kibum Kim
Keyword(s):  
Physical Activity ◽  
Human Health ◽  
Activity Recognition ◽  
Human Activity ◽  
Health Monitoring ◽  
Wearable Sensors ◽  
Gaussian Mixture ◽  
Sensor Data ◽  
Tree Classifier ◽  
Combined Features

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.

scholarly journals The Effectiveness of Go Green Implementation among Society in Kota Bharu, Kelantan

2016 ◽  
Vol 1 (1) ◽  
pp. 10
Author(s):  
Che Rusuli ◽  
Fitriani Q ◽  
Syahida N. ◽  
Hakim M.
Keyword(s):  
Environmental Management ◽  
Natural Resources ◽  
Human Health ◽  
Significant Relationship ◽  
The Body ◽  
Next Generation ◽  
Body Of Knowledge ◽  
Ways Of Life

Nowadays, Go Green has becoming an initiative in protecting the natural resources for the next generation and protects human health through environmental management and implementation of green ways of life. This study seeks to identify the effectiveness of the implementation of Go Green by Majlis Perbandaran Kota Bharu (MPKB) Kelantan. The population of this study includes society in Kelantan and 200 respondents will be involved which is lives in Pengkalan Chepa, Kubang Kerian, Pasir Tumboh, Wakaf Bharu and Kijang. The results from this study hoped will contribute to the body of knowledge which has a positive significant relationship with education, technologies and legal actions.

Next Generation Wearable Devices

2014 ◽  
Vol 6 (2) ◽  
pp. 25-51 ◽  
Author(s):  
Niraj Shakhakarmi
Keyword(s):  
Health Monitoring ◽  
Service Management ◽  
Wearable Sensors ◽  
Wearable Devices ◽  
Smart Devices ◽  
Smart Device ◽  
Monitoring Device ◽  
Next Generation ◽  
Spectrum Utilization ◽  
Smart Health

The next generation wearable devices are Smart health monitoring device and Smart sousveillance hat which are capable of using wearable sensors for measuring physiological information, sousveillanace, navigation, as well as smart device to smart device communications over cellular coverage. Smart health monitoring device collect and observe different health related information deploying biosensors and can predict health problems. Smart sousveillance hat provides the brainwaves based fatigue state, training and sousveillance around the wearer. The next generation wearable smart devices deploy the device to device communications in LTE assisted networks with D2D server, D2D Application server and D2D enhanced LTE signalling for D2D service management, spectrum utilization and broad cellular coverage, which make them portable, social, commercial and sustainable. Thus, the wearable device technology will merge with the smart communications besides the health and wellness. Furthermore, the simulation and performance evaluation shows that LTE-D2D wearable smart device communications provides two times more energy efficiency than LTE-UEs cellular communications. The LTE-D2D data rate is also found significantly higher with higher D2D-SINR for lower relative mobility (= 30m/s) and lower D2D distance (<400m) between devices.

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