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Fall Detection Nicholas Chan (EE) Abhishek Chandrasekhar (EE) Hahnming Lee (EE) Akshay Patel (CmpE)
Elderly Fall Statistics <ul><li>16,000 elderly Americans die from falling each year (CDC, 2005) </li></ul><ul><li>300,000 ...
Proposed Solution <ul><li>Two camera system executing custom algorithm: </li></ul><ul><li>Detect person in room </li></ul>...
Target Market Smart Hospital Rooms Nursing Homes & Clinics <ul><li>Our solution offers to  reduce injuries arising from fa...
Alternative Solutions <ul><li>Pressure-sensitive mats by the bed </li></ul><ul><li>Camera detection with optical flow algo...
Alternative Solution Problems <ul><li>Pressure sensitive mats have unavoidable edges that can  cause  falls </li></ul><ul>...
Technical Specifications <ul><li>Two webcams (Microsoft VX 6000) </li></ul><ul><ul><li>Resolution of 160x120 pixels </li><...
Camera Positioning <ul><li>Privacy is a major concern </li></ul><ul><li>Gaining maximal coverage from camera position is a...
Camera Positioning Maximal Coverage Head-level Camera High-level camera Coverage Area
Camera Positioning Maximal Privacy High-level camera Knee-level camera Coverage Area
Algorithm Overview <ul><li>Identify the region of an image occupied by the person </li></ul><ul><li>Ascertain the velocity...
Foreground Segmentation <ul><li>The background of every frame is subtracted </li></ul><ul><li>Statistical Gaussian model i...
Foreground Segmentation Foreground Segmentation Foreground Segmentation
Foreground Segmentation Foreground Segmentation
Largest Blob Detection <ul><li>Additional filtering is performed on the foreground-segmented image </li></ul><ul><li>The l...
Largest Blob Detection Blob Detection Blob Detection
Motion History Imaging <ul><li>Filtered foreground-segmented image data used to form Motion History Image (MHI) </li></ul>...
Motion History Imaging Swiftly Walking ( Medium C motion  ) Turning Around ( Low C motion  ) Falling ( High C motion  )
Elliptical Approximation Frame 1  Normal Walking Frame 150 Mid-Fall Change in  Ellipse Angle
Frame 120  Normal Walking Frame 150 Mid-Fall Change in  Eccentricity Elliptical Approximation
High Frequency Noise Possible Fall Elliptical Approximation
Statistical Analysis <ul><li>Falls result in:  1) high-velocity motion (high C motion  values) and 2) large statistical va...
Statistical Analysis C motion  > 0.65 σ θ   > 0.60
Call for Assistance <ul><li>Computer connected to Ethernet network </li></ul><ul><li>When fall happens a picture is taken ...
Call for Assistance UI Page refreshes every 5 seconds to check for screenshot on the server
Call for Assistance UI When a fall occurs a flashing red message along with a screenshot is displayed
Archiving Falls <ul><li>The shot can be archived with a date stamp onto the local server </li></ul><ul><li>The detected fa...
Results <ul><li>Results are based on evaluation of 30 falls and 20 non-falls </li></ul>Category % Success % Failure Falls ...
Problems and Solutions <ul><li>Hardware and Software Problems: </li></ul><ul><ul><li>MATLAB requires substantial memory to...
Real-Time Analysis <ul><li>Existing Problems: </li></ul><ul><ul><li>MATLAB is incapable of running threaded applications <...
Privacy Concerns <ul><li>Use of cameras brings in a major privacy concern </li></ul><ul><li>Different configurations are n...
Cost Analysis <ul><li>Assuming a rate of $28/hr, Engineer salaries would amount to $44,800 for 4 engineers during a 10 wee...
Future Improvements <ul><li>Enable support for multiple people </li></ul><ul><li>Improve speed of algorithm </li></ul><ul>...
Questions? <ul><li>16,000 Americans die from falling each year </li></ul><ul><li>300,000 elderly Americans have hip fractu...
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  1. 1. Fall Detection Nicholas Chan (EE) Abhishek Chandrasekhar (EE) Hahnming Lee (EE) Akshay Patel (CmpE)
  2. 2. Elderly Fall Statistics <ul><li>16,000 elderly Americans die from falling each year (CDC, 2005) </li></ul><ul><li>300,000 elderly Americans have hip fractures each year </li></ul><ul><li>90% of hip fractures result from falls </li></ul><ul><li>24% of elderly Americans who suffer hip fractures die within one year </li></ul><ul><li>40% of elderly women with hip fractures never walk unassisted again (National Osteoporosis Foundation) </li></ul>
  3. 3. Proposed Solution <ul><li>Two camera system executing custom algorithm: </li></ul><ul><li>Detect person in room </li></ul><ul><li>Perform statistical analysis of person’s motion </li></ul><ul><li>Determine if a fall has occurred </li></ul><ul><li>Send an alarm for help </li></ul><ul><li>Projected cost of $500 per room </li></ul>
  4. 4. Target Market Smart Hospital Rooms Nursing Homes & Clinics <ul><li>Our solution offers to reduce injuries arising from falls and to improve safety records at nursing homes and hospitals. </li></ul>
  5. 5. Alternative Solutions <ul><li>Pressure-sensitive mats by the bed </li></ul><ul><li>Camera detection with optical flow algorithm </li></ul><ul><li>RFID Solutions </li></ul><ul><li>Accelerometers (e.g., iLife ™) </li></ul>
  6. 6. Alternative Solution Problems <ul><li>Pressure sensitive mats have unavoidable edges that can cause falls </li></ul><ul><li>Optical flow analysis prone to errors arising from shadow artifacts </li></ul><ul><li>Potential EMI interference from RFID readers; RFID readers also very expensive (over $1000) </li></ul><ul><li>Accelerometer results in many false positives (e.g. a person sitting down quickly) </li></ul>
  7. 7. Technical Specifications <ul><li>Two webcams (Microsoft VX 6000) </li></ul><ul><ul><li>Resolution of 160x120 pixels </li></ul></ul><ul><ul><li>Video recorded at 15 frames per second </li></ul></ul><ul><li>Personal Computer to run algorithm: </li></ul><ul><ul><li>Intel Pentium Dual Core 2.5GHz Processor </li></ul></ul><ul><ul><li>3GB RAM </li></ul></ul><ul><ul><li>Standard Keyboard and Mouse </li></ul></ul>
  8. 8. Camera Positioning <ul><li>Privacy is a major concern </li></ul><ul><li>Gaining maximal coverage from camera position is also critical </li></ul><ul><li>A balance between these two must be achieved </li></ul>
  9. 9. Camera Positioning Maximal Coverage Head-level Camera High-level camera Coverage Area
  10. 10. Camera Positioning Maximal Privacy High-level camera Knee-level camera Coverage Area
  11. 11. Algorithm Overview <ul><li>Identify the region of an image occupied by the person </li></ul><ul><li>Ascertain the velocity of the person’s motion </li></ul><ul><li>Fit an ellipse to the person </li></ul><ul><li>Analyze the changes in the ellipses’ properties </li></ul><ul><li>Determine if a fall has occurred </li></ul>
  12. 12. Foreground Segmentation <ul><li>The background of every frame is subtracted </li></ul><ul><li>Statistical Gaussian model is generated for each pixel </li></ul><ul><li>HSV color space is used to minimize shadow effect </li></ul><ul><li>Pixels are labeled as either foreground or background based on a preset threshold </li></ul><ul><li>A binary foreground image is thus generated </li></ul>
  13. 13. Foreground Segmentation Foreground Segmentation Foreground Segmentation
  14. 14. Foreground Segmentation Foreground Segmentation
  15. 15. Largest Blob Detection <ul><li>Additional filtering is performed on the foreground-segmented image </li></ul><ul><li>The largest continuous cluster of pixels is detected and then isolated from the smaller clusters of noise </li></ul>
  16. 16. Largest Blob Detection Blob Detection Blob Detection
  17. 17. Motion History Imaging <ul><li>Filtered foreground-segmented image data used to form Motion History Image (MHI) </li></ul><ul><li>MHI used to quantify the velocity of the person’s motion </li></ul><ul><li>0 (zero velocity) ≤ C motion ≤ 1 (extreme velocity) </li></ul>
  18. 18. Motion History Imaging Swiftly Walking ( Medium C motion ) Turning Around ( Low C motion ) Falling ( High C motion )
  19. 19. Elliptical Approximation Frame 1 Normal Walking Frame 150 Mid-Fall Change in Ellipse Angle
  20. 20. Frame 120 Normal Walking Frame 150 Mid-Fall Change in Eccentricity Elliptical Approximation
  21. 21. High Frequency Noise Possible Fall Elliptical Approximation
  22. 22. Statistical Analysis <ul><li>Falls result in: 1) high-velocity motion (high C motion values) and 2) large statistical variance in elliptical orientation/eccentricity </li></ul><ul><li>Numerically, we define a fall is defined by: C motion > 0.65 and σ θ > 0.60 </li></ul><ul><li>These thresholds may vary slightly with camera position </li></ul>
  23. 23. Statistical Analysis C motion > 0.65 σ θ > 0.60
  24. 24. Call for Assistance <ul><li>Computer connected to Ethernet network </li></ul><ul><li>When fall happens a picture is taken </li></ul><ul><li>A fuzzy picture is stored to a local server </li></ul><ul><li>An updating intranet page is displayed at the nurse station </li></ul><ul><li>The page incorporates archiving features </li></ul><ul><li>Nurse analyzes picture and determines if a response is necessary </li></ul>
  25. 25. Call for Assistance UI Page refreshes every 5 seconds to check for screenshot on the server
  26. 26. Call for Assistance UI When a fall occurs a flashing red message along with a screenshot is displayed
  27. 27. Archiving Falls <ul><li>The shot can be archived with a date stamp onto the local server </li></ul><ul><li>The detected fall log shows a queue of falls that happened </li></ul><ul><li>On archiving and reloading the system shows normal status again </li></ul>
  28. 28. Results <ul><li>Results are based on evaluation of 30 falls and 20 non-falls </li></ul>Category % Success % Failure Falls 83.33 % 16.66 % Non-Falls 75 % 25 %
  29. 29. Problems and Solutions <ul><li>Hardware and Software Problems: </li></ul><ul><ul><li>MATLAB requires substantial memory to execute programs </li></ul></ul><ul><ul><li>Algorithm has difficulty accounting for auto-light adjustments by the webcam </li></ul></ul><ul><li>Solutions Proposed: </li></ul><ul><ul><li>Port existing algorithm to C++ in order to run it more efficiently; using C++ also removes the licensing hassles required with MATLAB </li></ul></ul><ul><ul><li>Light intensity can be normalized with histogram equalization techniques; alternatively use a webcam without light adjustment </li></ul></ul>
  30. 30. Real-Time Analysis <ul><li>Existing Problems: </li></ul><ul><ul><li>MATLAB is incapable of running threaded applications </li></ul></ul><ul><ul><li>Analysis and recording of video simultaneously is almost impossible as a result </li></ul></ul><ul><li>Solution: </li></ul><ul><ul><li>Use C++; Supports threading and memory management </li></ul></ul><ul><ul><li>Real time analysis is available via OpenCV library </li></ul></ul><ul><ul><li>Many MATLAB functions are implemented in the library </li></ul></ul>
  31. 31. Privacy Concerns <ul><li>Use of cameras brings in a major privacy concern </li></ul><ul><li>Different configurations are necessary for concealment </li></ul><ul><li>Terms & Conditions have to be included in hospital paperwork </li></ul><ul><li>The picture taken of the patient upon a fall is blurred </li></ul><ul><li>An option of not having the system on should be implemented if requested by the patient </li></ul>
  32. 32. Cost Analysis <ul><li>Assuming a rate of $28/hr, Engineer salaries would amount to $44,800 for 4 engineers during a 10 week development phase </li></ul><ul><li>Equipment Cost: </li></ul><ul><ul><li>$60 for two cameras </li></ul></ul><ul><ul><li>$270 for a modern Dell Inspiron 530 </li></ul></ul><ul><li>$170 Installation and Software Costs </li></ul><ul><li>Total Cost per Room = $500 </li></ul>
  33. 33. Future Improvements <ul><li>Enable support for multiple people </li></ul><ul><li>Improve speed of algorithm </li></ul><ul><li>Reduce false positives by making a self-learning system </li></ul><ul><li>Make the program standalone for easy deployment </li></ul><ul><li>Enable mainframe support for hospital with servers </li></ul>
  34. 34. Questions? <ul><li>16,000 Americans die from falling each year </li></ul><ul><li>300,000 elderly Americans have hip fractures each year </li></ul><ul><li>24% elderly Americans who suffer hip fractures die within one year </li></ul>Category % Success % Failure Falls 83.33 % 16.66 % Non-Falls 75 % 25 %
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