Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors
Abstract
:1. Introduction
2. Literature Review
2.1. Related Work
2.2. HAR System
3. Methods
3.1. Signal Processing Method for HAR
3.2. Database
3.3. Data Pre-Processing Filtering and Feature Extraction
3.4. Classification Techniques—Ensemble Methods
3.5. Performance Evaluation
4. Results and Discussion
4.1. Performance Evaluation of Dataset 1
4.1.1. Holdout and 10 Cross-Validations for Precision, Recall, F-measure, and ROC Evaluation
4.1.2. Holdout and 10-Fold Cross-Validations for Overall Accuracy Rate
4.2. Performance Evaluation of Dataset 2
4.2.1. Holdout Method for Precision, Recall, F-measure, and ROC Evaluation
4.2.2. 10-Fold Cross Validation for Precision, Recall, F-measure, and ROC Evaluation
4.2.3. Holdout and 10-Fold CROSS-validations for Overall Accuracy Rate Classification
4.3. Comparative Analysis
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Activity Reference | Description of Activity |
---|---|
A1 | Walking |
A2 | Walking upstairs |
A3 | Walking downstairs |
A4 | Sitting |
A5 | Standing |
A6 | Laying |
Feature | Description |
---|---|
Min | Smallest value in the array |
Max | Largest value in the array |
Std | Standard deviation |
Entropy | Signal entropy |
Kurtosis | Kurtosis of the frequency domain signal |
Skewness | Skewness of the frequency domain signal |
END (Holdout) | ||||||||
---|---|---|---|---|---|---|---|---|
SVM | RF | |||||||
Activity | Precision | Recall | F-measure | ROC | Precision | Recall | F-measure | ROC |
A1 | 96.10% | 97.60% | 96.90% | 99.20% | 92.70% | 94.90% | 93.80% | 99.70% |
A2 | 95.30% | 94.00% | 94.70% | 98.80% | 89.60% | 90.80% | 90.20% | 99.40% |
A3 | 95.50% | 96.00% | 95.70% | 98.80% | 92.30% | 91.90% | 92.10% | 99.50% |
A4 | 92.80% | 90.40% | 91.60% | 95.20% | 96.60% | 90.10% | 93.20% | 99.60% |
A5 | 88.80% | 91.50% | 90.20% | 96.20% | 88.80% | 94.60% | 91.60% | 99.40% |
A6 | 100.00% | 99.10% | 99.60% | 99.80% | 99.10% | 97.30% | 98.20% | 100.00% |
Random Subspace (10-fold Cross-Validation) | ||||||||
---|---|---|---|---|---|---|---|---|
SVM | Random Forest | |||||||
Activity | Precision | Recall | F-measure | ROC | Precision | Recall | F-measure | ROC |
A1 | 95.60% | 97.70% | 96.70% | 99.30% | 90.30% | 95.90% | 93.10% | 99.60% |
A2 | 95.40% | 94.80% | 95.10% | 98.70% | 92.50% | 90.00% | 91.20% | 99.30% |
A3 | 96.60% | 94.70% | 95.70% | 98.30% | 93.80% | 90.10% | 91.90% | 99.40% |
A4 | 93.00% | 93.90% | 93.40% | 98.20% | 96.40% | 94.20% | 95.30% | 99.80% |
A5 | 93.50% | 92.50% | 93.00% | 98.60% | 94.10% | 95.90% | 95.00% | 99.60% |
A6 | 99.00% | 99.40% | 99.20% | 99.80% | 98.00% | 98.20% | 98.10% | 100.00% |
Overall Accuracy Rate | |||
---|---|---|---|
Holdout | |||
Ensemble Method | SVM | RF | p-Value |
Bagging | 93.83% | 91.62% | 0.028 |
Adaboost | 94.24% | 94.24% | 0.917 |
Rotation forest | 89.95% | 92.23% | 0.344 |
END | 94.50% | 93.16% | 0.172 |
Random subspace | 94.24% | 92.76% | 0.116 |
Overall Accuracy Rate | |||
---|---|---|---|
10-Fold Cross-Validation | |||
Ensemble Method | SVM | Random Forest | p-Value |
Bagging | 94.57% | 92.88% | 0.173 |
Adaboost | 94.84% | 94.74% | 0.917 |
Rotation forest | 90.65% | 93.65% | 0.075 |
END | 95.14% | 94.48% | 0.249 |
Random subspace | 95.33% | 94.08% | 0.249 |
Random Subspace (Holdout) | ||||||
---|---|---|---|---|---|---|
SVM | RF | |||||
Activity | Precision | Recall | F-measure | Precision | Recall | F-measure |
A1 | 99.80% | 100.00% | 99.90% | 98.20% | 99.00% | 98.60% |
A2 | 98.90% | 99.50% | 99.20% | 98.20% | 98.60% | 98.40% |
A3 | 99.80% | 99.00% | 99.40% | 98.80% | 97.30% | 98.00% |
A4 | 96.70% | 97.20% | 97.00% | 96.60% | 95.30% | 95.90% |
A5 | 97.70% | 97.00% | 97.30% | 95.80% | 97.20% | 96.50% |
A6 | 100.00% | 100.00% | 100.00% | 100.00% | 99.80% | 99.90% |
Random Subspace (Holdout) | ||
---|---|---|
SVM | RF | |
Activity | ROC | ROC |
A1 | 1.000 | 1.000 |
A2 | 1.000 | 1.000 |
A3 | 1.000 | 1.000 |
A4 | 0.995 | 0.999 |
A5 | 0.998 | 0.999 |
A6 | 1.000 | 1.000 |
Random Subspace (10-Fold Cross-Validation Method) | ||||||
---|---|---|---|---|---|---|
SVM | RF | |||||
Activity | Precision | Recall | F-measure | Precision | Recall | F-measure |
A1 | 99.90% | 100.00% | 100.00% | 99.90% | 98.40% | 98.70% |
A2 | 99.70% | 99.70% | 99.70% | 97.50% | 99.20% | 98.30% |
A3 | 99.70% | 99.80% | 99.80% | 98.50% | 97.60% | 98.00% |
A4 | 97.90% | 98.00% | 98.00% | 97.00% | 95.20% | 96.10% |
A5 | 98.20% | 98.10% | 98.10% | 95.60% | 97.30% | 96.40% |
A6 | 100.00% | 100.00% | 100.00% | 100.00% | 99.80% | 99.90% |
Random Subspace (10-Fold Cross Validation) | ||
---|---|---|
SVM | RF | |
Activity | ROC | ROC |
A1 | 1.000 | 0.999 |
A2 | 1.000 | 0.999 |
A3 | 1.000 | 0.999 |
A4 | 0.999 | 0.998 |
A5 | 0.999 | 0.999 |
A6 | 1.000 | 1.000 |
Overall Accuracy Rate | |||
---|---|---|---|
Holdout | |||
Ensemble Method | SVM | RF | p-Value |
Bagging | 98.54% | 97.18% | 0.028 |
Adaboost | 98.43% | 98.07% | 0.686 |
Rotation forest | 98.07% | 98.03% | 0.893 |
END | 98.61% | 98.03% | 0.028 |
Random subspace | 98.74% | 97.86% | 0.028 |
Overall Accuracy Rate | |||
---|---|---|---|
10-Fold Cross-Validation | |||
Ensemble Method | SVM | RF | p-Value |
Bagging | 99.07% | 97.43% | 0.028 |
Adaboost | 99.17% | 98.82% | 0.028 |
Rotation forest | 98.43% | 98.22% | 0.249 |
END | 99.20% | 98.28% | 0.028 |
Random subspace | 99.22% | 97.91% | 0.028 |
Reference | Evaluation Method | Dataset | Classification Method | Overall Accuracy Rate |
---|---|---|---|---|
Proposed classifier | 10-fold Cross-validation | 10,000 samples | Random subspace-SVM | 99.22% |
Proposed classifier | Holdout | 10,000 samples | Random subspace-SVM | 98.74% |
Ronao and Cho (2017) [21] | 10-fold Cross-validation | 10,000 samples | Two stages of continuous Hidden Markov model | 93.18% |
Anguita et al. (2013) [25] | Holdout | 10,000 samples | OVA MC-SVM-Gaussian kernel | 96.5% |
Romero-Paredes et al. (2013) [24] | Holdout | 10,000 samples | OVO MC-SVM-Linear Kernel majority voting | 96.40% |
Kastner et al. (2013) [23] | Holdout | 10,000 samples | Kernel generalized learning vector quantization | 96.23% |
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Ku Abd. Rahim, K.N.; Elamvazuthi, I.; Izhar, L.I.; Capi, G. Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors. Sensors 2018, 18, 4132. https://doi.org/10.3390/s18124132
Ku Abd. Rahim KN, Elamvazuthi I, Izhar LI, Capi G. Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors. Sensors. 2018; 18(12):4132. https://doi.org/10.3390/s18124132
Chicago/Turabian StyleKu Abd. Rahim, Ku Nurhanim, I. Elamvazuthi, Lila Iznita Izhar, and Genci Capi. 2018. "Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors" Sensors 18, no. 12: 4132. https://doi.org/10.3390/s18124132
APA StyleKu Abd. Rahim, K. N., Elamvazuthi, I., Izhar, L. I., & Capi, G. (2018). Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors. Sensors, 18(12), 4132. https://doi.org/10.3390/s18124132