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)

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

    • Analysis of Biometric Data for Memory Augmentation using a SenseCam Eoin Lynch
    • Aim of the Project
      • To develop a system which can detect important events in a person’s daily life using biometric markers and other sensor data.
      • A daily summary in pictures can then be created for the user to review.
      • It is hoped that such a system if adequately developed in the future could be used to improve memory.
    • The SenseCam
      • The SenseCam is a wearable device that integrates a camera with sensor technology.
      • It is worn around the neck and during a 12 hour period will automatically capture about 2000 pictures.
      • The Sensors include
        • Passive infra red
        • Accelerometer
        • Light intensity
        • Temperature
    • The biometric sensors
      • The biometric sensors consist of a Bodymedia sensewear armband and a Polar heart rate monitor.
      • The Bodymedia armband show above, measures a number of biometric indicators..
      • The Polar heart rate monitor is shown below.
    • Process Diagram
    • Data Analysis Method
      • 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)
      • Points where the data crosses above the threshold are counted as particularly significant events.
        • For example, a high galvanic skin response would indicate high anxiety.
        • A high heat flux or step rate would be indicative of a period of exercise.
        • A change in the infra red intensity could indicate nearby person (due to body heat).
    • Sample data set showing threshold line
    • Image Processing
      • The SenseCam images can be analysed to determine which contain people.
      • Skin tones are segmented from the rest of the image.
      • Pixcels with skin are set to white. Those not are set to black.
      • If more than a certain coverage of white is detected after segmentation the presence of a person is assumed.
    • Results
      • Detection of emotionally intensive events is relatively straight forward,
      • 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.
      • This is due to the fact that many experiences can produce similar responses in basic biometric markers.
      • 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.
    • What next?
      • Segmenting the day into events and analysing these independently
      • Trying to establish classifiers for emotional states.
      • Subjecting the data to a more rigorous set of experiments to determine something more concrete.