Klasifikasi Physical Activity Berbasis Sensor Accelorometer, Gyroscope, dan Gravity menggunakan Algoritma Multi-class Ensemble GradientBoost

 (*)Firman Aziz Mail (Universitas Pancasakti, Makassar, Indonesia)
 Syahrul Usman (Universitas Pancasakti, Makassar, Indonesia)
 Jeffry Jeffry (Universitas Pancasakti, Makassar, Indonesia)

(*) Corresponding Author

Submitted: August 16, 2021; Published: October 26, 2021

Abstract

The current generation of smartphones is increasingly sophisticated, equipped with several sensors such as accelerometer, gravity sensor, and gyroscope that can be used to recognize human activities such as going up stairs, going down stairs, running and walking. To get information, the data will be grouped using statistical methods. The performance of statistical methods has shortcomings in classifying data because of the procedures that must be met. To cover this shortcoming, the ensemble technique is used. In this paper, we propose to apply the Multi-Class Ensemble Gradientboost algorithm to improve the performance of the logistic regression method in classifying such as climbing stairs, descending stairs, running and walking. The process of taking data using a smartphone by designing an Android-based .apk system. Then, the entire dataset was separated into training data and test data with a comparison percentage of 70:30. The results obtained show that the Multi-Class Ensemble Gradientboost algorithm succeeded in increasing the logistic regression performance by 27.93%

Keywords


Classification; Ensemble; Gradientboost; Logistic Regression; Smartphone

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References

M.-S. Chen, J. Han, and P. S. Yu, “Data mining: an overview from a database perspective,” IEEE Transactions on Knowledge and data Engineering, vol. 8, no. 6, pp. 866–883, 1996.

A. Barna, A. K. M. Masum, M. E. Hossain, E. H. Bahadur, and M. S. Alam, “A study on Human Activity Recognition Using Gyroscope, Accelerometer, Temperature and Humidity data,” in 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox’sBazar, Bangladesh, Feb. 2019, pp. 1–6. doi: 10.1109/ECACE.2019.8679226.

A. Nandy, J. Saha, C. Chowdhury, and K. P. D. Singh, “Detailed Human Activity Recognition using Wearable Sensor and Smartphones,” in 2019 International Conference on Opto-Electronics and Applied Optics (Optronix), Mar. 2019, pp. 1–6. doi: 10.1109/OPTRONIX.2019.8862427.

N. Kadri, A. Ellouze, and M. Ksantini, “Recommendation system for human physical activities using smartphones,” in 2020 2nd International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia, Oct. 2020, pp. 1–4. doi: 10.1109/ICCIS49240.2020.9257671.

S. Mekruksavanich and A. Jitpattanakul, “Exercise Activity Recognition with Surface Electromyography Sensor using Machine Learning Approach,” in 2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), Pattaya, Thailand, Mar. 2020, pp. 75–78. doi: 10.1109/ECTIDAMTNCON48261.2020.9090711.

A. K. Muhammad Masum, S. Jannat, E. H. Bahadur, Md. Golam Rabiul Alam, S. I. Khan, and M. Robiul Alam, “Human Activity Recognition Using Smartphone Sensors: A Dense Neural Network Approach,” in 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, May 2019, pp. 1–6. doi: 10.1109/ICASERT.2019.8934657.

H. Nematallah, S. Rajan, and A.-M. Cretu, “Logistic Model Tree for Human Activity Recognition Using Smartphone-Based Inertial Sensors,” in 2019 IEEE SENSORS, Montreal, QC, Canada, Oct. 2019, pp. 1–4. doi: 10.1109/SENSORS43011.2019.8956951.

C. Y. Shan, P. Y. Han, and O. S. Yin, “Deep Analysis for Smartphone-based Human Activity Recognition,” in 2020 8th International Conference on Information and Communication Technology (ICoICT), 2020, pp. 1–5.

T. Su, H. Sun, C. Ma, L. Jiang, and T. Xu, “HDL: Hierarchical Deep Learning Model based Human Activity Recognition using Smartphone Sensors,” in 2019 International Joint Conference on Neural Networks (IJCNN), Jul. 2019, pp. 1–8. doi: 10.1109/IJCNN.2019.8851889.

Md. Z. Uddin and J. Torresen, “Activity Recognition Using Smartphone Sensors, Robust Features, and Recurrent Neural Network,” in 2019 13th International Symposium on Medical Information and Communication Technology (ISMICT), Oslo, Norway, May 2019, pp. 1–6. doi: 10.1109/ISMICT.2019.8743759.

M. Zhang, “Gait Activity Authentication Using LSTM Neural Networks with Smartphone Sensors,” in 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN), Shenzhen, China, Dec. 2019, pp. 456–461. doi: 10.1109/MSN48538.2019.00092.

C. Zhang and Y. Ma, Ensemble machine learning: methods and applications. Springer, 2012.

N. Hardiyanti, A. Lawi, and F. Aziz, “Classification of Human Activity based on Sensor Accelerometer and Gyroscope Using Ensemble SVM method,” in 2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT), 2018, pp. 304–307.

A. Lawi, F. Aziz, and S. L. Wungo, “Increasing accuracy of classification physical activity based on smartphone using ensemble logistic regression with boosting method,” in Journal of Physics: Conference Series, 2019, vol. 1341, no. 4, p. 042002.

F. Aziz, “Klasifikasi Aktivitas Manusia menggunakan metode Ensemble Stacking berbasis Smartphone,” Journal of System and Computer Engineering (JSCE), vol. 1, no. 2, pp. 53–58, 2021.

W. Rindler, Essential relativity: special, general, and cosmological. Springer Science & Business Media, 2012.

R. F. Tinder, “Relativistic flight mechanics and space travel,” Synthesis lectures on engineering, vol. 1, no. 1, pp. 1–140, 2006.

G. Milette and A. Stroud, Professional Android sensor programming. John Wiley & Sons, 2012.

S. Kabai, “Oldham coupling, The Wolfram Demonstrations Project.” 2008.

D. W. Hosmer Jr, S. Lemeshow, and R. X. Sturdivant, Applied logistic regression, vol. 398. John Wiley & Sons, 2013.

F. Provost and R. Kohavi, “Guest editors’ introduction: On applied research in machine learning,” Machine learning, vol. 30, no. 2, pp. 127–132, 1998.

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