Fall Detection

Nicholas Chan (EE)Abhishek Chandrasekhar (EE)

Hahnming Lee (EE)Akshay Patel (CmpE)

1

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)

2

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

2

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 %