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Home Fall, Distress Monitor

Fall Final Presentation ECE/CE Senior Design Team 1927 James Balducci, ECE Heather Fabian, ECE Ke Ma, CE

Faculty Advisor: Dr. Necmi Biyikli Project Sponsor: Cigna Sponsor Contact: Dr. Steve Mastrianni IT Senior Principal and Director Cigna Corp., 900 Cottage Grove Road, Wilde 1-0010, Bloomfield, CT 06152

Collaborators

Outline

1. Problem 2. Requirements 3. Considerations 4. Solution 5. How Walabot Works 6. Fall Detection & Categorization 7. Timeline 8. Budget 9. Comments/Questions

Problem

• “America’s 65-and-over population is projected to nearly double over the next

three decades, from 48 million to 88 million by 2050.” NIH

• People want to remain in their own homes
• Information can aid professionals in treating patients
• Caregivers are often adult children
• Cannot be around 24/7

Requirements

• Monitor wellbeing of individuals
• Privacy of sensitive information
• User-Friendly
• Not Cumbersome
• Relatively inexpensive

Data Collection Devices Considered

XBox Kinect Sensor Apple Watch FitBit Walabot Pros

• Open Source
• Heart Rate Monitoring
• Accelerometer
• Heart Rate Monitoring
• Open Source
• Accelerometer
• Heart Rate Monitoring
• Open Source
• No cameras

→ Privacy

• ~ 5m range

Cons

• ~ 4 ft range
• RGB-D Camera

→ Privacy issue

• Cigna didn’t

recommend

• Wearable

→ Charging → Remembering → Anxiety

• Not Open Source
• Wearables

→ Charging → Remembering → Anxiety

• Constant power
• Single room
• No memory

Solution

10:43

Caregivers’ View Customer’s View Alert!:

A fall has been detected!

Our Team’s Focus: Fall Detection and Categorization

Walabot- Overview

Walabot: Fall Detection

Step 1: Data Collection with Walabot Step 2: Making the Heatmaps Step 3: Choosing Optimal Heatmaps Step 4: Location of person: Intersection between Vertical and Horizontal Planes Step 5: Fall Detection

Walabot: Collecting Data

The raw data from the walabot represents the impulse response It provides us with the Time-of-Flight (TOF) profile. TOF Profile: the time it takes the transmitted waves to reach the reflector and then travel back to the receiver.

• Two Types Heatmaps:

Vertical Plane Heatmaps: y-z plane Horizontal Plane Heatmaps: x-z plane

Walabot: Making Heatmaps

Walabot: Making Heatmaps

Four Parameters: 1) The frame number for the stabilized signals 2) The antenna pair 3) The upper limit of range 4) The lower limit of range

Step 1: Data Collection with Walabot Step 2: Making the Heatmaps Step 3: Choosing Optimal Heatmaps Step 4: Location of person: Intersection between Vertical and Horizontal Planes Step 5: Fall Detection

We are going to Examine the maximum signal intensity value of the signals by looking at the color-bar, and choose the heatmap of the antenna pair with the highest intensity value. And when we know which heatmaps to use, we can look at their intersection points to localize the person.

Our Team’s Focus: Fall Detection and Categorization

Categorizing

Position 3: Standing upright Position 2: Mid Level Position 1: Ground Level Sudden Fall S h

• r

t F a l l

t t

Timeline

Winter Break Goals

• Write Experiment

→ Find appropriate 𝚬t values → Possible new fall categories → Recognize fall patterns

• Complete Experiment
• Create Spring Timeline

Budget

Walabot: $ 599.00 Microsoft Surface: $ 799.00 Total Value: $ 1,398.00

References

https://pixabay.com/en/man-silhouette-human-standing-147090/ https://commons.wikimedia.org/wiki/File:Silhouette_of_man_on_chair.svg https://svgsilh.com/image/2705600.html https://commons.wikimedia.org/wiki/File:Xbox-360-Kinect-Standalone.png https://pixabay.com/en/apple-watch-series-4-apple-3674940/ https://www.flickr.com/photos/149902454@N08/35069751323

Thank you! Comments/Questions welcome!