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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
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• 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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Experimental Results
• 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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Experimental Results
• 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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Experimental Results
• 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
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 ü ü ü ü ü ü ü ü ü ü ü ü ü
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Experimental Results
• 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
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Experimental Results
Actual Motion Mode
Predicted Motion Mode
90.5% 9.5%
2.1% 97.9%
Average Recall Rate: 94.2%
Actual Motion Mode
Predicted Motion Mode
90.6% 9.4%
1.3% 98.7%
Average Recall Rate: 94.65%
GNSS-Outaged Trajectories
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Experimental Results
Actual Motion Mode
Predicted 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%
Average Recall Rate: 94.77%
Actual Motion Mode
Predicted 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%
Average Recall Rate: 93.825%
GNSS-Outaged Trajectories
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Experimental Results
Actual Motion Mode
Predicted Motion Mode
97.2% 2.8%
15.8% 84.2%
Average Recall Rate: 90.7%
Actual Motion Mode
Predicted Motion Mode
90.2% 9.8%
26.9% 73.1%
Average Recall Rate: 81.65%
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Experimental Results
Actual Motion Mode
Predicted Motion Mode
96.2% 3.8%
5.9% 94.1%
Average Recall Rate: 95.15%
Actual Motion Mode
Predicted Motion Mode
90.2% 9.8%
21.7% 78.3%
Average Recall Rate: 84.25%
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Experimental Results
Actual Motion Mode
Predicted Motion Mode
87.07% 12.93%
30.94% 69.06%
Average Recall Rate: 78.06%
Actual Motion Mode
Predicted Motion Mode
90.22% 9.78%
30.28% 69.72%
Average Recall Rate: 79.97%
GNSS-Outaged Trajectories
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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
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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
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