Presentation

Navigation and Instrumentation Research Group

Mostafa ElhoushiPh.D. Candidate, Queen’s University, Kingston, Canada

Department of Electrical & Computer Engineering

PhD Thesis Defense

Advanced Motion Mode Recognition for Portable Navigation

05/02/2023

NavINST Group - Queen's University 2

• This work was done under collaboration with Trusted Positioning Inc. (TPI) - later acquired by InvenSense Inc. - and Royal Military College (RMC) of Canada through funds from Mitacs, NSERC CRD grant, and TPI.

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Contents

• Introduction– Background– Motivation– Problem Statement– Objective– Prior Work– Contributions

• Proposed Methodology• Experimental Results• Summary

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Introduction

Background• Navigation:

– the techniques of determining the position, velocity, and attitude of a moving body

• Portable Navigation:– the modified navigation techniques for a person

moving using a portable navigation device• Applications of Portable Navigation:

– cell phone navigation for trips– cell phone localization for advertising– dismounted soldier and first responder

localization– handheld portable surveying systems in urban

environments

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Introduction

Portable Navigation• GNSS is very accurate BUT:

– Not available indoors and urban canyons– Power consuming

• Other absolute positioning methods are less accurate and need infrastructure

• Other relative positioning methods use sensors which are erroneous

• Solution: Sensor Fusion

WiFiRFID Gyroscopes

Accelerometers

Magnetometer

GNSS

Barometer

Cell towers

Relative MeasurementsAbsolute Measurements

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Introduction

Motivation• Portable navigation is becoming increasingly popular• Accurate, reliable, cost effective personal positioning

is needed• There is a need for seamless outdoor/indoor portable

navigation• A variety of commercial and military applications

need such technology

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Introduction

Problem Statement• The portable device (e.g., cell phone)

is untethered– able to move freely without constraints

within another moving platform (person or vehicle)

• The system has to be environment independent, i.e., work seamlessly outdoor/indoor/urban

• The system has to work autonomously in different modes of transit (e.g., walking, driving, train, elevator, …)

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Introduction

Objective• Automatically detect the following

Motion Modes of a portable device on a user/platform:– for various device types– utilizing low-cost MEMS sensors– in real-time– for arbitrary Device Usage– for arbitrary orientation of the device– for an arbitrary user/platform

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Introduction

Need for Motion Mode Recognition• Each motion mode can have its own

optimized navigation algorithm/constraints

Detect Motion Mode

Walking PDR with Walking Parameters

Running PDR with Running Parameters

Driving Driving Algorithms

Stationary Apply Zero-Update Velocity

ElevatorFix 2D Position

If Map Matching Used -> Correct 2D Position

Escalator Correct Along Track Distance/Position

… …

Motion ModeAlgorithm / Constraints /

Optimization

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Introduction

Prior Work• Most of prior work had limited

robustness– portable navigation device tethered in

much of the work– covered limited device usages– covered limited orientations

• Most of prior work covered a small number of motion modes

• Some depended on:– GNSS signal availability → don’t work

indoors– Wi-Fi positioning → need special

infrastructure

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Introduction

Contributions• Detected large number of motion

modes• Detected for the first time new

motion modes:

• Recognition is robust:– independent to device usage– independent to device orientation

• Implemented in real-time on consumer devices

• Only mandatory inputs are signals from self-contained low-cost sensors

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Methodology

• Pattern recognition is used to detect motion modes.

• The figure shows the steps of the pattern recognition process.

• The next slides explain what is performed in each step in the pattern recognition process.

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Methodology

• Data Inputs

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Methodology

• Pre-Processing– Raw sensor readings have little meaning– Need to process them to come up with

more meaningful variables• Levelled Vertical Acceleration: • Magnitude of Levelled Horizontal Plane

Acceleration: • Compensated Norm of Angular Rotation

Components: • Vertical Velocity: differentiation of smoothed

altitude

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• Feature Extraction – Statistical Features

• mean, median, mode, variance, standard deviation, 75th percentile, inter-quartile range, average absolute difference, and binned distribution, skewness, kurtosis

– Energy, Power, and Magnitude Features• energy, sub-band energies, sub-band energy ratios, and signal

magnitude area– Time-Domain Features

• zero-crossing rate and number of maximum peaks– Frequency-Domain Features

• absolute values of short-time Fourier transform, FOS spectral analysis, power spectral centroid, frequency domain entropy, average of continuous wavelet transform

– Other• cross-correlation between leveled vertical and horizontal

acceleration components• ratio between vertical velocity and number of peaks in levelled

vertical acceleration

Methodology

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• Classification – Classification method used is Decision

Tree – Optimized using pruning

Methodology

Land-Based VesselBicycle

Land-Based Vessel F5>Thr5

F2>Thr2 F3>Thr3

F1>Thr1

F6>Thr6

Walking

Running

Bicycle

Walking F4>Thr4Bicycle Running

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Methodology

• Classification (cont.)– Separate classifiers for various groups of

motion modes– Required classifier invoked based on

certain conditions

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Methodology

• Post-Classification Refining– In the full solution, further enhancements

are used after machine learning, such as: • Majority Selection• Context Exclusion• Map Information• GNSS Velocity

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Experimental Results

• Data Collection

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• Data Collection (cont.)

Data Collected>2300 trajectories, >225 hours

divided into:

Training Data Evaluation DataFeatures extracted from it are used to train classifier to generate classifier model

Features extracted from it are fed into generated classifier model to evaluate its performance

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• Data Collection (cont.)– Various users with various genders,

heights, weights, motion dynamics, speeds, and gaits

– Various device usages, and orientations– Different Elevators/Escalators/Moving

Walkways in different places and cities– Land-based vessels included:

• Different types of: Car, Truck, Bus, Train, Light-rail Train

• Sitting, Standing, and placing device On Platform

• Moving in busy areas, quiet neighborhoods, and highways in different cities

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• Data Collection (cont.)– Device Usages covered:

Handheld

Hand Still by Side

Pocket/Thigh

Ear

Belt Holder

Dangling

Arm Band

Chest

Leg

Wrist / Smartwatch

Backpack

Goggle/Smart glasses

Purse

Laptop Bag

Bicycle Handle

Bicycle Holder

Car Dashboard

Car Drawer

Car Box bet. Seats

Car Holder

On Seat

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ü ü ü ü ü ü ü ü ü ü ü ü

ü ü ü ü ü ü ü ü ü ü ü

ü ü ü ü ü ü ü ü ü ü

ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü

05/02/2023



• Results shown are of Classification only– i.e., without Post-Classification Refining

• Performance Measure is Confusion Matrices of evaluating classifiers on Evaluation Data (not Training Data)– Another evaluation was made on

trajectories after inserting GNSS outage

05/02/2023



Actual Motion Mode

Predicted Motion Mode

90.5% 9.5%

2.1% 97.9%

Average Recall Rate: 94.2%

Actual Motion Mode


90.6% 9.4%

1.3% 98.7%


GNSS-Outaged Trajectories

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Actual Motion Mode


96.5% 2.2% 1.1% 0.3%

0.4% 99.4% 0.2% 0.0%

1.4% 1.9% 92.0% 4.8%

0.3% 0.0% 8.4% 91.2%


Actual Motion Mode


95.2% 0.7% 4.0% 0.2%

0.1% 98.3% 1.6% 0.0%

2.6% 1.3% 91.4% 4.8%

1.7% 0.1% 7.8% 90.4%



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Actual Motion Mode


97.2% 2.8%

15.8% 84.2%


Actual Motion Mode


90.2% 9.8%

26.9% 73.1%


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Actual Motion Mode


96.2% 3.8%

5.9% 94.1%


Actual Motion Mode


90.2% 9.8%

21.7% 78.3%


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Actual Motion Mode


87.07% 12.93%

30.94% 69.06%


Actual Motion Mode


90.22% 9.78%

30.28% 69.72%



05/02/2023


Summary

• Conclusion– Robust detection of wide range of motion modes

is possible with self-contained low-cost sensors• GNSS unavailability had minor – if not positive - effect

– Decision trees best choice for real-time:• high accuracy and low computation requirements

– Solution commercialized and implemented in consumer devices in the market

• Future Work– Improve detection of:

– Add new motion modes• E.g., Crawling, Airplane, Marine-Based Vessel

05/02/2023


Publications

Patents:[1] Inventors: M. Elhoushi, J. Georgy, and A. Noureldin, Assignee: Invensense Inc., “Method and System for Estimating Multiple Modes of Motion”, U.S. Serial No. 14/528,868, filing date: 30 October 2014.Accepted Journal Publications:[2] M. Elhoushi, J. Georgy, A. Noureldin, and M. Korenberg, “Motion Mode Recognition for Indoor Pedestrian Navigation using Portable Devices,” in IEEE Transactions on Instrumentation and Measurement

Published Conference Publications:[3] M. Elhoushi, J. Georgy, A. Wahdan, M. Korenberg, and A. Noureldin, “Using Portable Device Sensors to Recognize Height Changing Modes of Motion,” in 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2014, pp. 477 – 481.[4] M. Elhoushi, J. Georgy, M. Korenberg, and A. Noureldin, “Robust Motion Mode Recognition for Portable Navigation Independent on Device Usage,” in 2014 IEEE/ION Position, Location and Navigation Symposium - PLANS, 2014, pp. 158–163.[5] M. Elhoushi, J. Georgy, M. Korenberg, and A. Noureldin, “Broad Motion Mode Recognition for Portable Navigation,” in Proceedings of the 27th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2014), 2014, pp. 1768–1773.Submitted Journal Publications:[6] M. Elhoushi, J. Georgy, A. Noureldin, and M. Korenberg, “Online Motion Mode Recognition for Portable Navigation using Low-Cost Sensors,” in NAVIGATION[7] M. Elhoushi, J. Georgy, A. Noureldin, and M. Korenberg, “A Survey on Approaches of Motion Mode Recognition Using Sensors,” in IEEE Transactions on Intelligent Transportation Systems

05/02/2023


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