Adaptive Body Area Networks Using Kinematics and Biosignals

Adaptive Sampling-Based Information Collection for Wireless Body Area Networks

To collect important health information, WBAN applications typically sense data at a high frequency. However, limited by the quality of wireless link, the uploading of sensed data has an upper frequency. To reduce upload frequency, most of the existing WBAN data collection approaches collect data with a tolerable error. These approaches can guarantee precision of the collected data, but they ar...

Prediction of Whole-body Lifting Kinematics Using Artificial Neural Networks

DTMP: Energy Consumption Reduction in Body Area Networks Using a Dynamic Traffic Management Protocol

Advances in medical sciences with other fields of science and technology is closely casual profound mutations in different branches of science and methods for providing medical services affect the lives of its descriptor. Wireless Body Area Network (WBAN) represents such a leap. Those networks excite new branches in the world of telemedicine. Small wireless sensors, to be quite precise and calc...

Biosignals learning and synthesis using deep neural networks

BACKGROUND Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biome...

Body Area Networks: A Survey

Advances in wireless communication technologies, such as wearable and implantable biosensors, along with recent developments in the embedded computing area are enabling the design, development, and implementation of body area networks. This class of networks is paving the way for the deployment of innovative healthcare monitoring applications. In the past few years, much of the research in the ...