Human Activity Recognition Using Accelerometer on Smartphones Symposium 2014 Human Activity Recognition Using Accelerometer on Smartphones Akram Bayat, Dr. Marc Pomplun, Dr. Duc A.Tran

Introduction: 1- Define the problem Symposium 2014 Introduction: 1- Define the problem

Introduction : Motivation Symposium 2014 Introduction : Motivation Human activity recognition is an important and challenging research area with many applications in healthcare, smart environments and surveillance and security.

Introduction : Computer vision-based techniques Computer vision-based techniques have been widely used for human activity tracking, but they mostly require infrastructure support. Using smartphones that can be used under the conditions of daily living is a big advantage.  

Introduction : Some application Symposium 2014 Introduction : Some application In-Building Localization with Smartphones [1]

Introduction : some application Symposium 2014 Introduction : some application Handling digital entities with the feet[2] Extract both feet tracking (pose) and movement recognition such as kicking, sliding and rotating.

Introduction : Motivation of this study Symposium 2014 Introduction : Motivation of this study If people answered honestly to the question, 'What are the reasons why you exercise?', a frequent answer would be to burn calories[3].  On a growing scale we use mobile phones for diverse activities in our daily life, such as entertainment, education or information purposes. Type of Activity Burning how many calories Our Motivation is to use smartphone to track the user physical activity and estimate his energy expenditure using 3axis accelerometer

Introduction : the Contributions of this work Symposium 2014 Introduction : the Contributions of this work Activities of our study include some that not have been widely been studied ( e.g. slow versus fast walking, aerobic dancing) Less sensory input than existing work, yet able to obtain a comparable accuracy …

What we have done in this work : Data Collection Symposium 2014 What we have done in this work : Data Collection List of Activities Running Slow- Walk Fast – Walk Aerobic Dancing Stairs- Up Stairs- Down Acceleration Data collecting Accelerometer data reader App[4] Overall 79,573 samples Frequency 100 Hz

Data Collection Fig. 4. A presentation of tri-axial accelerometer data for a typical subject for different activities

Raw Data Preparation 𝐴 𝑥 , 𝐴 𝑦 , 𝐴 𝑧 Digital Low Pass Filter Accelerometer generates 3-time series along x-axis, y-axis and z-axis: 𝐴 𝑥 , 𝐴 𝑦 , 𝐴 𝑧 𝐴 𝐷𝐶 𝑖 = [𝐴 𝑖 +24× 𝐴 𝐷𝐶 𝑖−1 ] 25 𝐴 𝐴𝐶 =𝐴− 𝐴 𝐷𝐶 Digital Low Pass Filter Digital High Pass Filter Gravitational Acceleration : Dc components Body Acceleration : AC components 𝐴 𝑥_𝐷𝐶 𝐴 𝑦_𝐷𝐶 𝐴 𝑧_𝐷𝐶 𝐴 𝑥_𝐴𝐶 𝐴 𝑦_𝐴𝐶 𝐴 𝑧_𝐴𝐶

Creating times series data Compute the magnitude of acceleration: 𝐴 𝑚 10 time series 𝐴 𝑚 𝐴 𝒙 𝐴 𝒚 𝐴 𝒛 𝐴 𝑥_𝐷𝐶 𝐴 𝑦_𝐷𝐶 𝐴 𝑧_𝐷𝐶 𝐴 𝑥_𝐴𝐶 𝐴 𝑦_𝐴𝐶 𝐴 𝑧_𝐴𝐶

Feature extraction : Windowing overlapping Fig. 5. Acceleration plots for the six activities along the z-axis that captures the forward movements. All six activities exhibit periodic behavior but have distinctive pattern. We observe that Running and Fast-Walking exhibit very similar pattern.

Size of window : windowing overlapping 50% of overlapping 128 samples 128 Good performance

Features RMS : Root Mean Squared MinMax value : difference between maximum and minimum for each window. Mean : Average on each axis over a time period Standard deviation Correlation between different pairs of axes 43 dimensional feature vector

Selecting top 5 best classifiers Combination of classifiers Classification Individual classifiers Selecting top 5 best classifiers Combination of classifiers

Individual classifiers Accuracy Multilayer perceptron 89.48 % LibSVM 88.76% Random Forest 87.55% LMT 85.89% Logit Boost 82.54%

Confusion Matrix 65 1 3 40 2 84 24 5 4 8 114 47 Dancing Stairs_Down Slow_walk Running Stairs_up Fast_walk 65 1 3 40 2 84 24 5 4 8 114 47

F-measure for each Activity of four best classifiers Best performance for each activity is obtained for Multilayer Perceptron

Classifier fusion Combining multiple good classifiers can improve accuracy, efficiency and robustness over single classifiers The method to combine the classifiers in this work is average of probabilities Classifiers Accuracy Multilayer Perceptron, LogitBoost, LibSVM 91.15% Multilayer Perceptron, LogitBoost,LibSVM, LMT 90.90% Multilayer Perceptron, LogitBoost,LibSVM, Random Forest Multilayer Perceptron, LogitBoost 88.51% Multilayer Perceptron, LibSVM 88.27% Multilayer Perceptron, LogitBoost, LibSVM, Random Forest, LMT 81.10%

conclusion

Thank you

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