fall detection nicholas chan (ee) abhishek chandrasekhar (ee) hahnming lee (ee) akshay patel (cmpe)...
TRANSCRIPT
Fall Detection
Nicholas Chan (EE)Abhishek Chandrasekhar (EE)
Hahnming Lee (EE)Akshay Patel (CmpE)
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Elderly Fall Statistics
16,000 elderly Americans die from falling each year (CDC, 2005)
300,000 elderly Americans have hip fractures each year
90% of hip fractures result from falls
24% of elderly Americans who suffer hip fractures die within one year
40% of elderly women with hip fractures never walk unassisted again (National Osteoporosis Foundation)
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Proposed Solution
Two camera system executing custom algorithm:
1. Detect person in room
2. Perform statistical analysis of person’s motion
3. Determine if a fall has occurred
4. Send an alarm for help
Projected cost of $500 per room
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Target Market
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Smart Hospital Rooms Nursing Homes & Clinics
Our solution offers to reduce injuries arising from falls and to improve safety records at nursing homes and hospitals.
Alternative Solutions
Pressure-sensitive mats by the bed
Camera detection with optical flow algorithm
RFID Solutions
Accelerometers (e.g., iLife ™)
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Alternative Solution Problems
Pressure sensitive mats have unavoidable edges that can cause falls
Optical flow analysis prone to errors arising from shadow artifacts
Potential EMI interference from RFID readers; RFID readers also very expensive (over $1000)
Accelerometer results in many false positives (e.g. a person sitting down quickly)
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Technical Specifications
Two webcams (Microsoft VX 6000) Resolution of 160x120 pixels Video recorded at 15 frames per second
Personal Computer to run algorithm: Intel Pentium Dual Core 2.5GHz Processor 3GB RAM Standard Keyboard and Mouse
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Camera Positioning
Privacy is a major concern
Gaining maximal coverage from camera position is also critical
A balance between these two must be achieved
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Camera PositioningMaximal Coverage
Head-level Camera
High-level camera
Coverage Area
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Camera PositioningMaximal Privacy
High-level camera
Knee-level camera Coverage Area
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Algorithm Overview
1. Identify the region of an image occupied by the person
2. Ascertain the velocity of the person’s motion
3. Fit an ellipse to the person
4. Analyze the changes in the ellipses’ properties
5. Determine if a fall has occurred
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Foreground Segmentation
The background of every frame is subtracted
Statistical Gaussian model is generated for each pixel
HSV color space is used to minimize shadow effect
Pixels are labeled as either foreground or background based on a preset threshold
A binary foreground image is thus generated
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Foreground Segmentation
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Foreground Segmentation
Foreground Segmentation
Foreground Segmentation
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Foreground Segmentation
Largest Blob Detection
Additional filtering is performed on the foreground-segmented image
The largest continuous cluster of pixels is detected and then isolated from the smaller clusters of noise
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Largest Blob Detection
Blob Detection
Blob Detection
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Motion History Imaging
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Filtered foreground-segmented image data used to form Motion History Image (MHI)
MHI used to quantify the velocity of the person’s motion
0 (zero velocity) ≤ Cmotion ≤ 1 (extreme velocity)
Motion History Imaging
Swiftly Walking( Medium Cmotion )
Turning Around( Low Cmotion )
Falling( High Cmotion )
Cmotion numbergray pixels
numbergray number white pixels18
Elliptical Approximation
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Frame 1
Normal Walking
Frame 150
Mid-FallChange in
Ellipse Angle
Frame 120
Normal Walking
Frame 150
Mid-FallChange in
Eccentricity20
Elliptical Approximation
0 50 100 150 200 250 300 350 400 4500
0.5
1
1.5
2
Frame
Elli
pse
Ang
le (r
ad)
Angle
0 50 100 150 200 250 300 350 400 4501
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3
4
Frame
Elli
pse
Ecc
entri
city
Eccentricity
High Frequency Noise
Possible Fall
Elliptical Approximation
Statistical Analysis
Falls result in: 1) high-velocity motion (high Cmotion values) and2) large statistical variance in elliptical orientation/eccentricity
Numerically, we define a fall is defined by:Cmotion > 0.65 and σθ > 0.60
These thresholds may vary slightly with camera position
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Statistical Analysis
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Cmotion > 0.65
σθ > 0.60
Call for Assistance
Computer connected to Ethernet network
When fall happens a picture is taken
A fuzzy picture is stored to a local server
An updating intranet page is displayed at the nurse station
The page incorporates archiving features
Nurse analyzes picture and determines if a response is necessary
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Call for Assistance UI
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Page refreshes every 5 seconds to check for screenshot on the server
Call for Assistance UI
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When a fall occurs a flashing red message along with a screenshot is displayed
Archiving Falls
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The shot can be archived with a date stamp onto the local server
The detected fall log shows a queue of falls that happened
On archiving and reloading the system shows normal status again
Results
Results are based on evaluation of 30 falls and 20 non-falls
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Category % Success % Failure
Falls 83.33 % 16.66 %
Non-Falls 75 % 25 %
Problems and Solutions
Hardware and Software Problems: MATLAB requires substantial memory to execute programs Algorithm has difficulty accounting for auto-light adjustments
by the webcam
Solutions Proposed: Port existing algorithm to C++ in order to run it more
efficiently; using C++ also removes the licensing hassles required with MATLAB
Light intensity can be normalized with histogram equalization techniques; alternatively use a webcam without light adjustment
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Real-Time Analysis
Existing Problems: MATLAB is incapable of running threaded applications Analysis and recording of video simultaneously is
almost impossible as a result
Solution: Use C++; Supports threading and memory
management Real time analysis is available via OpenCV library Many MATLAB functions are implemented in the library
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Privacy Concerns
Use of cameras brings in a major privacy concern
Different configurations are necessary for concealment
Terms & Conditions have to be included in hospital paperwork
The picture taken of the patient upon a fall is blurred
An option of not having the system on should be implemented if requested by the patient
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Cost Analysis
Assuming a rate of $28/hr, Engineer salaries would amount to $44,800 for 4 engineers during a 10 week development phase
Equipment Cost: $60 for two cameras $270 for a modern Dell Inspiron 530
$170 Installation and Software Costs
Total Cost per Room = $500
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Future Improvements
Enable support for multiple people
Improve speed of algorithm
Reduce false positives by making a self-learning system
Make the program standalone for easy deployment
Enable mainframe support for hospital with servers
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Questions?
16,000 Americans die from falling each year
300,000 elderly Americans have hip fractures each year
24% elderly Americans who suffer hip fractures die within one year
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Category % Success % Failure
Falls 83.33 % 16.66 %
Non-Falls 75 % 25 %