Dead Reckoning with Smart Phone
Sensors for Emergency Rooms
Ravi Pitapurapu, Ajay Gupta, Kurt Maly, Tameer Nadeem,
Ramesh Govindarajulu, Sandip Godambe, and Arno Zaritsky
Contact: [email protected]
ICOST 2015 10th - 12th June 2015 Geneva
Outline
 Introduction
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Lean and Spaghetti Diagram
Indoor Positioning Systems
 Indoor
 Dead
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
and Background
Positioning System Challenges
Reckoning Algorithm
Stride Detection
Stride Length Estimation
Change in User direction with each Stride
 Path
Correction
 Summary
ICOST 2015 10th - 12th June 2015 Geneva
 Lean

Lean is quality improvement philosophy process to
maximize customer value and minimize waste
 Spaghetti
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Diagram
Tracks user movement on floor
Detect unnecessary or long paths
Rearrange equipment
New paths for optimal layout
ICOST 2015 10th - 12th June 2015 Geneva
 Current
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Indoor Positioning System
GPS - becomes uncertain indoors
Wi-Fi triangulation – high uncertainty
RFID - costly
 Challenges
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Devise a cost effective system
Devise a robust and accurate system
Minimal impact on the infrastructure
Shouldn’t hinder the activities of users at work place
 Solution
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System fuses smart phone sensors and other information
such as Wi-Fi
ICOST 2015 10th - 12th June 2015 Geneva
 Dead
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Reckoning or Deduced Reckoning
‘Process of calculating one’s current position by using a
previously determined or known position, and advancing
that position based upon known or estimated
measurements over elapsed time and course’.
Sensors on a smart phone
 Accelerometer
 Gyroscope
Dead Reckoning Algorithm
 Stride Detection
 Stride Length Estimation
 Change in user direction at each stride
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 Stride
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Detection
Accelerometer and Gyroscope data readings
Accelerometer is very sensitive hence results in noisy data
Basic signal processing to filter out noise
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 Stride
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Detection continued..
Gyro scope readings are much smoother accounting for the
slightest force exerted on the device
Use accelerometer readings to validate gyro data for
optimal decision making
ICOST 2015 10th - 12th June 2015 Geneva
 Stride
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Detection continued..
Processing the gyroscope readings to obtain a sinusoidal
wave
Use a Butterworth filter to smooth the data
Processed gyro readings are fed to a Kalman filter to cancel
noise
The result is a smooth sinusoidal wave; use to infer the
strides = distance between any two consecutive peaks
ICOST 2015 10th - 12th June 2015 Geneva
 Stride
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Length Estimation
Personalized stride length model takes into account gait
characteristics of an individual
Stride patterns and lengths will be different for different
individuals at different speeds
General observation – slower walks smaller stride length and
faster walks larger stride length
Model function between time elapsed per stride and
distance covered in stride by quadratic function
Calibrate personalized approximation function by having a
user walk at different speed fixed distances
During operation: from stride detection obtain time elapsed
for a stride, look up distance in approximation function
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ICOST 2015 10th - 12th June 2015 Geneva
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 Change
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Use quaternions to represent the orientation of phone in 3D space using roll, pitch and yaw of the gyro scope
The overall change in the users heading is calculated from
the previous state using standard quaternion operations
 Path
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in user direction with each stride
Correction
Map representation - represent floor through its walls,
doors, furniture and other obstacles to paths
Error Detection –intersection of current path with obstacles
such as walls
ICOST 2015 10th - 12th June 2015 Geneva
Path correction

-
continued
Error correction
 Corridor – path close to corridor segment then replace
with corridor path
 Door – if path turns and close to door then replace with
path through door
 Backtracking – if come to dead end, use back tracking to
reverse prior decisions, e.g. instead of using door
correction use corridor correction
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Backtracking correction
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Results – CHKD emergency room path
Measured and corrected path at CHKD
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 Summary
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The overall objective was to lay a foundation for an
accurate and robust indoor positioning system using inertial
sensors on mobile phones
With the measuring components unable to achieve correct
paths we were able to achieve correct paths in all
experiments at ODU and CHKD by adding path correction
modules.
Future work includes the addition of reporting modules that
will allow the analyst to produce various plots selected from
various users at various times and make that information
available through the web to various devices.
ICOST 2015 10th - 12th June 2015 Geneva
 Stride
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Detection Continued..
Considering the phone is kept in pocket the forces which we
measure are the pitch and the roll
A step is considered the duration between the foot toes
leaving the floor to the touchdown of the heel.
The root mean square of pitch+roll is going to be the
maximum when the user takes a step and when the stride
continues on the other leg the pitch+roll value is the
minimum.
So we can infer a minimum followed by a maximum again
followed by a minimum as two steps or a stride.
Sampling threshold to avoid false steps (No two stirdes can
happen in 30 samples)
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