== Body Sensor Networks ==

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=== Introduction ===

Biological data is increasingly easy to collect with the development of simpler and cheaper biosensors. This type of data has important implications for the future of healthcare, health monitoring, and physiologically integrated technology. The goal of this project is to develop an integrated platform for the analysis of various types of biological data, which can be used to classify and analyze new data, as well as employ biological data for practical applications ranging from diagnosis to physiologically responsive devices, and more.


=== Project Overview ===

In order to accurately classify and analyze biological data, a number of functions are needed. In particular, known characteristic patterns visible in data such as EEG (electroencephalography) or EKG (electrocardiography) must be recognized by the system in order to make reasonable decisions. 

The recognition of such patterns requires statistical manipulation of the data in order to identify important features. 

The current focus of this project is to research appropriate transformations that can be applied to data in order to extract key features. These features can then be analyzed by an algorithm trained on datasets exhibiting characteristic patterns to classify novel data.


=== Tools/ Resources === 

[http://www.cs.waikato.ac.nz/ml/weka/ Weka]

[http://www.r-project.org/ The R Project for Statistical Computing]

[http://www.arduino.cc/ Arduino]

[http://openbci.com/ OpenBCI]

[https://www.cooking-hacks.com/documentation/tutorials/ehealth-biometric-sensor-platform-arduino-raspberry-pi-medical Libelium e-Health Sensor Platform]