swimming tracker - motion recognition
TRANSCRIPT
©2015 GlobalLogic Inc.
Swimming Tracker:
Motion Recognition
Orest Hera
©2015 GlobalLogic Inc.
Introduction
Raw data processing
Swimming data analysis
01
02
03
Introduction:● Objectives● MEMS sensors● Classification problems● Sensor-based activity recognition
01
Introduction
Objectives
● Swimming and resting detection● Lap counting● Swim style recognition● Minimal usage of RAM of microcontroller
Introduction
MEMS (MicroElectroMechanical System) sensors● 3D Accelerometer● 3D Gyroscope● 3D Magnetometer
Introduction
Applications of MEMS sensors
● Mobile devices (Android, iOS)○ Display/map orientation
○ Step counter, Compass applications
○ Augmented reality
● Small custom devices○ Small vehicle navigation and stabilization (quadcopter)
○ Industrial automation
○ Innovative smart systems
Introduction
Pattern recognition tools
● Image recognition open source software:○ OpenCV library
○ Many specialized tools (face, poses, hands tracking)
● Speech recognition open source software:○ CMU Sphinx (HMM)
○ Julius (HMM 3-gram)
○ Kaldi (Deep neural network)
● Sensor-based activity recognition:○ Custom classifiers
Introduction
Sensor-based activity recognition
● Accelerometer“Activity recognition from accelerometer data” / N.Ravi, N.Dandekar, P.Mysore, M.L.Littman, IAAI’05 Proceedings, Vol. 3, 1541-1546, 2005
● Gyroscope and AccelerometerA Public Domain Dataset for Human Activity Recognition Using Smartphones / D.Anguita, A.Ghio, L.Oneto, X.Parra and J.L.Reyes-Ortiz, ESANN 2013. Bruges, Belgium 24-26 April 2013 (UCI Machine Learning Repository)
○ 30 subjects performing activities of daily living
○ 561-feature vector with time and frequency domain variables for fixed-width window
○ 10299 labeled instances
● Techniques of Natural Language Processing
Introduction
Activity recognition from accelerometer data
Classifiers:● Decision Trees● K-nearest neighbors● SVM● Naive Bayes
Accuracy:● Multiple subjects cross-validated: 92 - 99%● One subject training, same subject another data for testing: 70 - 90%● One subject training, another subject for testing: 46 - 73%
Raw data processing:● Gravity force detection by accelerometer● Rotation speed by gyroscope● Complementary filter● Compass
02
Raw data processing
Raw sensor data processing
Angle by vector of gravity force
Raw data processing
Advantages:● Direct measurement without
error accumulation
Disadvantages:● System own acceleration should
be filtered● Relaxation time due to Low-pass
filter
Raw data processing
Angle by rotation speed integration
Advantages:● Can be used during accelerated motion
Disadvantages:● Integration error accumulation
Raw data processing
Complementary filter
Advantages:● High frequency by gyroscope● Low frequency by gravity force vector
Disadvantages:● Cannot compensate error accumulation drift of rotations around vector of
gravity force
Expected
Magnetometer raw data
Raw data processing
Reality
Raw data processing
Magnetic distortions
Distortion types:● Hard iron (permanent magnet)● Soft iron (easily magnetized and demagnetized)
Types of compensating methods:● Offline (least squares methods)● Real-time adaptive (Kalman filter, neural networks,...)
Raw data processing
Magnetometer calibrationEllipsoid equation:
The least-squares problem Pseudo-inverse matrix
Swimming data analysis:● Device orientation● Chains of motion subactions● Probabilistic classification
04
Swimming data analysis
X-,Z-axes horizontal direction (yaw)
Swimming data analysis
X-,Y-,Z-axes vertical direction (pitch)
Raw data processing
Chains of motion subactions
Dictionary of motion subactions: [A, B, C, D,...]● Typical for swimming: A, B,...● Typical for resting: C, D,...
Temporal chains of moves:● Swimming: AABAAAABABAAABABBAA● Swimming: ACACCACAACCACACCACC● Resting: ADADDADAADDADADDADD● Resting: ACACCCCACCCACCCCACC
Probabilistic classification
Swimming data analysis
● Input data points
Probabilistic classification
Swimming data analysis
● Input data points● Expectation-maximization
Probabilistic classification
Swimming data analysis
● Input data points● Expectation-maximization
Probabilistic classification
Swimming data analysis
● Input data points● Expectation-maximization● Probability distribution
Swimming data analysis
Results
©2015 GlobalLogic Inc.
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