Analysis of Biometric Data for Memory Augmentation using a SenseCam (midterm)


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Analysis of Biometric Data for Memory Augmentation using a SenseCam (midterm)

  1. 1. Analysis of Biometric Data for Memory Augmentation using a SenseCam Eoin Lynch
  2. 2. Aim of the Project <ul><li>To develop a system which can detect important events in a person’s daily life using biometric markers and other sensor data. </li></ul><ul><li>A daily summary in pictures can then be created for the user to review. </li></ul><ul><li>It is hoped that such a system if adequately developed in the future could be used to improve memory. </li></ul>
  3. 3. The SenseCam <ul><li>The SenseCam is a wearable device that integrates a camera with sensor technology. </li></ul><ul><li>It is worn around the neck and during a 12 hour period will automatically capture about 2000 pictures. </li></ul><ul><li>The Sensors include </li></ul><ul><ul><li>Passive infra red </li></ul></ul><ul><ul><li>Accelerometer </li></ul></ul><ul><ul><li>Light intensity </li></ul></ul><ul><ul><li>Temperature </li></ul></ul>
  4. 4. The biometric sensors <ul><li>The biometric sensors consist of a Bodymedia sensewear armband and a Polar heart rate monitor. </li></ul><ul><li>The Bodymedia armband show above, measures a number of biometric indicators.. </li></ul><ul><li>The Polar heart rate monitor is shown below. </li></ul>
  5. 5. Process Diagram
  6. 6. Data Analysis Method <ul><li>Raw sensor data is read into Matlab.The datasets are smoothed and a threshold line for significant events is calculated using a specially designed algorithm. (Kapur method) </li></ul><ul><li>Points where the data crosses above the threshold are counted as particularly significant events. </li></ul><ul><ul><li>For example, a high galvanic skin response would indicate high anxiety. </li></ul></ul><ul><ul><li>A high heat flux or step rate would be indicative of a period of exercise. </li></ul></ul><ul><ul><li>A change in the infra red intensity could indicate nearby person (due to body heat). </li></ul></ul>
  7. 7. Sample data set showing threshold line
  8. 8. Image Processing <ul><li>The SenseCam images can be analysed to determine which contain people. </li></ul><ul><li>Skin tones are segmented from the rest of the image. </li></ul><ul><li>Pixcels with skin are set to white. Those not are set to black. </li></ul><ul><li>If more than a certain coverage of white is detected after segmentation the presence of a person is assumed. </li></ul>
  9. 9. Results <ul><li>Detection of emotionally intensive events is relatively straight forward, </li></ul><ul><li>Determining which emotion is present is not. Further sensor data from different sources such as muscle electromyography or facial expression detection are likely to be necessary to accomplish this. </li></ul><ul><li>This is due to the fact that many experiences can produce similar responses in basic biometric markers. </li></ul><ul><li>In most instances it was clear from the image, to the user, why the program may have regarded it as a significant event. eg.Talking to someone important, eating, running, watching an interesting film. </li></ul>
  10. 10. What next? <ul><li>Segmenting the day into events and analysing these independently </li></ul><ul><li>Trying to establish classifiers for emotional states. </li></ul><ul><li>Subjecting the data to a more rigorous set of experiments to determine something more concrete. </li></ul>