Body Sensor Networks

Table of Contents

  1. 2015 Winlab Summer Internship
    1. Projects
    1. Indoor Localization
    2. Introduction
      1. Motivation
      2. What is ORBIT Lab?
      3. Overall Approach
      4. Resources
      5. Procedure
      6. Plan of Action
      7. Weekly Presentations
      8. Team
    1. SDR in ORBIT: Spectrum Sensing
      1. Introduction
      2. Team
      3. Objectives
      4. Weekly Progress
      5. Experiments
    1. LTE Unlicensed (LTE-U)
      1. Introduction
      2. Objectives
      3. Theory
      4. Analyzing Tools
      5. Experiment 1: Transmit and Receive LTE Signal
      6. Experiment 2: The Waterfall Plot
      7. Experiment 3: eNB and UE GUI
      8. Experiment 4: Varying Bandwidths
      9. Experiment 5: Working with TDD or FDD
      10. Experiment 6: TDD with Varying Bandwidths
      11. Experiment 7: TDD Waterfall Plot
      12. Poster
      13. Members
      14. Materials
      15. Resources
    1. Distributed Simulation of Power Grid
      1. Introduction
      2. Objectives
      3. People
      4. Resources
    1. Context-Aware IoT Application on MobilityFirst
      1. Introduction
      2. Objectives
      3. System Architecture
      4. Network Diagram
      5. Experiment Tools
      6. Results
      7. Future Work
      8. Team member
    1. Real-Time Cyber Physical Systems Application on MobilityFirst
      1. Github Repo
      2. Introduction
      3. Preliminary Goal
      4. Outline of the Project
      5. Tasks
      6. Image Processing
      7. Weekly Summary
      8. Team
      9. Presentation Slides
    1. GNRS Assited Inter Domain Routing
      1. Introduction
    1. GNRS Management
      1. Introduction
      2. Work Milestones
    1. Effective Password Cracking Using GPU
      1. Introduction
      2. Objectives
      3. GPU
      4. Experiment
      5. Tools and Resources
  2. Body Sensor Networks
    1. Introduction
    2. Project Overview
    3. Data Collection
      1. Initial BCI data
    4. Data Analysis
    5. Tools/ Resources
    1. Unity Traffic Simulation
      1. Introduction
      2. Objectives
      3. People
    1. Mobile Security
      1. Introduction
      2. Motivation
    2. Resources
  3. Dynamic Video Encoding
    1. Introduction
    2. Goals
    3. Background Information
      1. Anatomy of a Video File
      2. What is a CODEC?
      3. H.264 Compression Algorithm
      4. Scalable Video Coding
      5. Network Emulator Test Results
      6. DASH Multi-Bitrate Encoding
      7. DASH Content Generation
      8. Bitrate Profiles
      9. Video Encoding Algorithms
      10. GPAC
    4. Presentations
    5. People


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.

Data Collection

Data was collected using the OpenBCI open source bioelectric recording platform.

Initial BCI data

Experimental setup:

The images below show a general EEG headplot, mapping commonly used electrode locations, as well as a marked-up headplot showing the electrode locations used for collecting the data for this experiment.

The colors correspond to electrode colors, which correspond to graphics created by the OpenBCI GUI. Below is a sample image of data collection in the OpenBCI GUI. The black and white ear electrodes (A1 and A2) are ground and reference electrodes for the others.

Electrode locations were chosen for relevance to movement/ motion intention. C3, Cz, and C4 are associated with sensorimotor integration. F3 and F4 are associated with motor planning. These are the central nodes. Fp1 and Fp2 are associated with attention and judgement, while Fz is associated with working memory.

Data collected was as follows:

5 samples rest (10 sec each)

10 samples up

10 samples down

10 samples right

10 samples left

(up, down, left, right represent direction of imagined motion; each sample includes 3 sec prep and 3 sec imagined movement)

Data Analysis

Data analysis (for EEG/ BCI data) is being done through EEGLAB and BCILAB toolboxes for MATLAB (from the Scwartz Center for Computational Neuroscience). Sample data was initially used to create channel spectra maps (and other data visualizations) using the EEGLAB interface.

Tools/ Resources


The R Project for Statistical Computing



Libelium e-Health Sensor Platform



Last modified 2 years ago Last modified on 07/27/15 13:32:08

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