Akram Bayat, Dr. Marc Pomplun, Dr. Duc A.Tran

Human Activity Recognition Using Accelerometer on Smartphones

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Introduction: 1- Define the problem S

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Introduction : Motivation

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

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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

In-Building Localization with Smartphones [1]

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Introduction : some application

Handling digital entities with the feet[2]

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Extract both feet tracking (pose) and movement recognition such as kicking, sliding and rotating.

Introduction : Motivation of this studyS

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Our Motivation is to use smartphone to track the user physical activity and estimate his energy expenditure using 3axis accelerometer

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

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

…

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What we have done in this work : Data Collection

Acceleration Data collecting

List of Activities

RunningSlow- WalkFast – WalkAerobic DancingStairs- UpStairs- Down

Accelerometer data reader App[4] Overall 79,573 samples Frequency 100 Hz

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Data Collection

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

Raw Data Preparation

Accelerometer generates 3-time series along x-axis, y-axis and z-axis:

Digital Low Pass Filter Digital High Pass Filter

𝐴𝐷𝐶 𝑖=

[𝐴¿¿ 𝑖+24×𝐴𝐷𝐶𝑖 −1]

25¿ 𝐴𝐴𝐶=𝐴−𝐴𝐷𝐶

Body Acceleration : AC components

Gravitational Acceleration : Dc components

𝐴𝑦𝐷𝐶𝐴𝑧𝐷𝐶

𝐴𝑥 𝐴𝐶𝐴𝑦 𝐴𝐶𝐴𝑧 𝐴𝐶

𝐴𝑥𝐷𝐶

Creating times series data

Compute the magnitude of acceleration:

𝐴𝑥𝐷𝐶𝐴𝑦𝐷𝐶

𝐴𝑧𝐷𝐶𝐴𝑥 𝐴𝐶𝐴𝑦 𝐴𝐶

𝐴𝑧 𝐴𝐶

10 time series

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

128

50% of overlapping

128 samp

les

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

Classification

Selecting top 5 best classifiers

Individual classifiers

Combination of classifiers

Individual classifiers

Classifier Accuracy

Multilayer perceptron 89.48 %

LibSVM 88.76%

Random Forest 87.55%

LMT 85.89%

Logit Boost 82.54%Multilayer Perceptron LibSVM

Random Forest LMT

Logit Boost

78

80

82

84

86

88

90

Accuracy

Accuracy

Multilayer Percep-tron LibSVM

Random ForestLMT

Logit Boost

78

80

82

84

86

88

90

Accuracy

Confusion Matrix

Dancing Stairs_Down Slow_walk Running Stairs_up

Fast_walk

65 1 1 1 3 0 Dancing

1 40 2 0 2 0 Stairs_Down

0 0 84 0 2 0 Slow_walk

3 2 0 24 1 1 Running

5 4 8 1 114 1 Stairs_up

1 1 1 0 2 47 Fast_walk

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 90.90%

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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away from the talk.

Emphasize the Contributions of your Paper