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GAIT MATE

03/10/22

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CS342 Building for Digital Health

Midterm Presentation

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Project Background

1 in 4 older americans fall every year.

Falls are very common

Our mission: reduce downstream costs related to complex fall-related injuries and thereby capture missed revenue

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Despite the high risk of falls, identification has remained elusive

Hard to identify falls

creating an at home functional mobility assessment (fma) to pilot among 200 adults

FMA pilot

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A way to assess fall risk

in older adults

to provide fall prevention resources

and interventions.

Our Need Statement

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Project Roadmap

ResearchKit Baseline

Survey and Firebase

01/30/22

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Project Roadmap

ResearchKit Baseline

Survey and Firebase

01/30/22

CareKit Schedule & ResearchKit Gait Task

02/10/22

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Project Roadmap

ResearchKit Baseline

Survey and Firebase

01/30/22

CareKit Schedule & ResearchKit Gait Task

02/10/22

Report A Fall Feature

02/10/22

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Project Roadmap

ResearchKit Baseline

Survey and Firebase

01/30/22

CareKit Schedule & ResearchKit Gait Task

02/10/22

Report A Fall Feature

02/10/22

Midterm Presentation

02/15/22

© 2016 Stanford Byers Center for Biodesign

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Project Roadmap

ResearchKit Baseline

Survey and Firebase

01/30/22

CareKit Schedule & ResearchKit Gait Task

02/10/22

Report A Fall Feature

02/10/22

Update

Gait Task & Data to Firebase

02/26/22

Midterm Presentation

02/15/22

© 2016 Stanford Byers Center for Biodesign

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Project Roadmap

ResearchKit Baseline

Survey and Firebase

01/30/22

CareKit Schedule & ResearchKit Gait Task

02/10/22

Report A Fall Feature

02/10/22

Update

Gait Task & Data to Firebase

02/26/22

Midterm Presentation

02/15/22

UI / UX Design &

Push Notifications

03/04/22

© 2016 Stanford Byers Center for Biodesign

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Project Roadmap

ResearchKit Baseline

Survey and Firebase

01/30/22

CareKit Schedule & ResearchKit Gait Task

02/10/22

Update

Gait Task & Data to Firebase

02/26/22

Midterm Presentation

02/15/22

Report A Fall Feature

02/10/22

UI / UX Design &

Push Notifications

03/04/22

User Testing & ML Fall Prediction

03/06/22

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Project Roadmap

ResearchKit Baseline

Survey and Firebase

01/30/22

CareKit Schedule & ResearchKit Gait Task

02/10/22

Update

Gait Task & Data to Firebase

02/26/22

Midterm Presentation

02/15/22

Report A Fall Feature

02/10/22

UI / UX Design &

Push Notifications

03/04/22

User Testing & ML Fall Prediction

03/06/22

Final Presentation

03/10/22

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Project Architecture

ResearchKit

CareKit

iPhone

sensors

Frontend

SwiftUI

Backend

iOS application

Input

Data

Display Results

User

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Demo

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Accelerometer Data

Cleaned using a low-pass filter through Fourier Transform

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ML Fall Risk Identification

  • Currently no labeled fall risk data set using accelerometer data
  • Data set - 30,000 steps of accelerometer data from 118 individuals
  • Conducted unsupervised pre-training using contrastive learning to cluster gait data
  • Defined an “average gait representation”
  • 22 times better accuracy than random assignment

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Gait

Normal Gait

Falling Gait

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Gait

Shaking Gait

Hopping Gait

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Cosine Similarity Derived Metrics

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The gate to walking with your mates.

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The gate to walking with your mates

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© 2016 Stanford Byers Center for Biodesign

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© 2016 Stanford Byers Center for Biodesign