scholarly journals Towards an open data framework for body sensor networks supporting bluetooth low energy

2016 ◽  
Author(s):  
Ninoshka K. Singh ◽  
Darnell O. Ricke
Keyword(s):  
Sensor Networks ◽  
Open Data ◽  
Low Energy ◽  
Body Sensor Networks ◽  
Bluetooth Low Energy ◽  
Data Framework

scholarly journals Synchronization in wireless biomedical-sensor networks with Bluetooth Low Energy

2015 ◽  
Vol 1 (1) ◽  
pp. 73-76 ◽  
Author(s):  
André Bideaux ◽  
Bernd Zimmermann ◽  
Stefan Hey ◽  
Wilhelm Stork
Keyword(s):  
Sensor Networks ◽  
Standard Deviation ◽  
Low Energy ◽  
Time Difference ◽  
Sensor Nodes ◽  
Body Sensor Networks ◽  
Average Delay ◽  
Bluetooth Low Energy ◽  
Transmission Delays ◽  
Wireless Body Sensor Networks

AbstractBluetooth Low Energy (BLE) has reduced the energy consumption for sensor nodes drastically. One major reason for this improvement is a non-continuous connection between the nodes. But this causes also a nondeterministic data transmission time. Most synchronization protocols are influenced by this characteristic, with the result of less accuracy. In wireless body sensor networks this accuracy is often of vital importance. Therefore this paper evaluates different synchronization principles customized for BLE.For the evaluation measurements we used two BLE modules connected to one micro controller. This setup allowed us to calculate the error directly for the different principles. First we measured the send-receive time as a reference which influences most synchronization protocols. This time is directly affected by random transmission delays of BLE. Second we used the time difference between receiving and acknowledging a message as principle (A). The last principle (B) can only be used between nodes that use BLE that don’t require a constant connection, because it needs to connect and disconnect the nodes. After a new connection the “connected” events occur in the BLE nodes almost at the same time and can be used for synchronization. The reference measurement showed the worst results. The average delay was 4.76 ms with a standard deviation of 2.32 ms. Principle (A) showed average delays of 7.51 ms, which was almost exactly 1 connection interval in our setup. The standard deviation was 0.41 ms. Principle (B) showed the best results with an average time difference of 39.92 μs and a standard deviation of 14.19 μsThe results showed that with the principles (A) and (B) the synchronization of nodes can be highly improved compared to the reference. In future we will test the principles with synchronization protocols in real sensor nodes also with respect to the processor load.

scholarly journals Towards an Open Data Framework for Body Sensor Networks Supporting Bluetooth Low Energy

2016 ◽  
Author(s):  
Ninoshka K. Singh ◽  
Darrell O Ricke
Keyword(s):  
Health Status ◽  
New Technologies ◽  
Emotional Health ◽  
Open Data ◽  
Data Access ◽  
Heterogeneous Data ◽  
Low Energy ◽  
Easy Access ◽  
Bluetooth Low Energy ◽  
Data Framework

AbstractMajor companies, healthcare professionals, the military, and other scientists and innovators are now sensing that fitness and health data from wearable biosensors will likely provide new discoveries and insights into physiological, cognitive, and emotional health status of an individual. Having the ability to collect, process, and correlate data simultaneously from a set of heterogonous biosensor sources may be a key factor in informing the development of new technologies for reducing health risks, improving health status, and possibly preventing and predicting disease. The challenge in achieving this is getting easy access to heterogeneous data from a set of disparate sensors in a single, integrated wearable monitoring system. Often times, the data recorded by commercial biosensing devices are contained within each manufacturer’s proprietary platform. Summary data is available for some devices as free downloads or included only in annual premium memberships. Access to raw measurements is generally unavailable, especially from a custom developed application that may include prototype biosensors. In this paper, we explore key ideas on how to leverage the design features of Bluetooth Low Energy to ease the integration of disparate biosensors at the sensor communication layer. This component is intended to fit into a larger, multi-layered, open data framework that can provide additional data management and analytics capabilities for consumers and scientists alike at all the layers of a data access model which is typically employed in a body sensor network system.

scholarly journals Bluetooth low energy sensor networks for railway applications

2017 ◽  
Author(s):  
Angela Hernandez ◽  
Antonio Valdovinos ◽  
David Perez-Diaz-de-Cerio ◽  
Jose Luis Valenzuela
Keyword(s):  
Sensor Networks ◽  
Low Energy ◽  
Bluetooth Low Energy ◽  
Energy Sensor

scholarly journals From Sensor Networks to Internet of Things. Bluetooth Low Energy, a Standard for This Evolution

Sensors ◽  
2017 ◽  
Vol 17 (2) ◽  
pp. 372 ◽  
Author(s):  
Diego Hortelano ◽  
Teresa Olivares ◽  
M. Ruiz ◽  
Celia Garrido-Hidalgo ◽  
Vicente López
Keyword(s):  
Sensor Networks ◽  
Internet Of Things ◽  
Low Energy ◽  
Bluetooth Low Energy

scholarly journals Enhanced Low Energy Communication Protocol for Implantable Wireless Body Sensor Networks

2013 ◽  
Vol 75 (11) ◽  
pp. 26-29
Author(s):  
Maryam Elazhari ◽  
Ahmed Toumanari ◽  
Rachid Latif ◽  
Nadya El moussaid
Keyword(s):  
Sensor Networks ◽  
Communication Protocol ◽  
Low Energy ◽  
Body Sensor Networks ◽  
Wireless Body Sensor Networks

scholarly journals Avaliação de técnicas de inteligência computacional para identificação de atividades de vida diária

2018 ◽  
Author(s):  
Wylken S. Machado ◽  
Pedro H. Barros ◽  
Eliana S. Almeida ◽  
Andre L. L. Aquino
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sensor Networks ◽  
Random Forest ◽  
Decision Tree ◽  
Nearest Neighbors ◽  
Low Energy ◽  
Support Vector ◽  
Body Sensor Networks ◽  
K Nearest Neighbors

Neste trabalho apresentamos a avaliação do desempenho de algoritmos de machine learning para identificar Atividades de Vida Diária (ADLs) e quedas. Nós avaliamos os seguintes algoritmos: K-Nearest Neighbors, Naive Bayes, Support Vector Machine, Decision Tree, Random Forest, Extra-Trees e Redes Neurais Recorrentes. Utilizamos um conjunto de dados coletados por uma Body Sensor Networks com cinco dispositivos sensores conectados através da interface Bluetooth Low Energy, chamado UMAFall. Obtivemos resultados satisfatórios, principalmente para as atividades saltar e queda frontal, com 100 % de acurácia, utilizando o algoritmo Extra-Trees.

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