Classification of Human Activity Recognition Utilizing  Smartphone Data of CNN-LSTM

Widya Rizka Ulul Fadilah; Wahyu Andhyka  Kusuma; Agus Eko Minarno; Yuda Munarko

doi:10.22219/kinetik.v6i2.1319

Issue

Vol. 6, No. 2, May 2021

Issue Published : May 31, 2021

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Classification of Human Activity Recognition Utilizing Smartphone Data of CNN-LSTM

https://doi.org/10.22219/kinetik.v6i2.1319

Widya Rizka Ulul Fadilah

Universitas Muhammadiyah Malang

Wahyu Andhyka Kusuma

Universitas Muhammadiyah Malang

Agus Eko Minarno

Universitas Muhammadiyah Malang

Yuda Munarko

Universitas Muhammadiyah Malang

Corresponding Author(s) : Yuda Munarko

yuda@umm.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 6, No. 2, May 2021
Article Published : May 31, 2021

Abstract

Human activity recognition has been applied in various areas of life by utilizing the gyroscope and accelerometer sensors embedded in smartphones. One of the functions of recognizing human activities is by understanding the pattern of human activity, thereby minimizing the possibility of unexpected incidents. This study classified of human activity recognition through CNN-LSTM on the UCI HAR dataset by applying the divide and conquer algorithm. This study additionally employs tuning hyperparameter to obtain the best accuracy value from the parameters and the proposed architecture. From the test results with the CNN-LSTM method, the accuracy rate for dynamic activity is 99.35%, for static activity is 96.08%, and the combination of the two models is 97.62%.

Keywords

Classification, Human Activity Recognition, CNN-LSTM, Hypermeter, Divide, Conquer

Fadilah, W. R. U., Kusuma, W. A. ., Minarno, A. E., & Munarko, Y. (2021). Classification of Human Activity Recognition Utilizing Smartphone Data of CNN-LSTM. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 6(2). https://doi.org/10.22219/kinetik.v6i2.1319

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References

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References

M. Danuri, “Perkembangan dan Transformasi Teknologi Digital,” infocam, vol. XV, no. II, pp. 116–123, 2019.

GE Purna Sastriya, DC Khrisne, and Made Surdarma, “Aplikasi Asisten Untuk Lansia Dengan Memanfaatkan Smartphone Berbasis Android,” SINTECH (Science Inf. Technol. J., vol. 2, no. 2, pp. 63–70, 2019. https://doi.org/10.31598/sintechjournal.v2i2.315

O. Ockikiriyanto, " Rancang Bangun Tempat Tidur Pasien Otomatis Dengan Sensor Accelerometer Gyroscope Untuk Mengatur Keseimbangan Berbasis Mikrokontroler Arduino," Cyclotron, vol. 2, no. 2, 2019. https://doi.org/10.30651/cl.v2i2.3256

Haniah Mahmudah, Okkie Puspitorini, Nur Adi Siswandari, Ari Wijayanti, and Eliya Alfatekha, " Metode Naive Bayes Classifier – Smoothing pada Sensor Smartphone untuk Klasifikasi Aktivitas Pengendara," J. Nas. Tech. Electrical and Technol. inf., vol. 9, no. 3, pp. 268–277, 2020. https://doi.org/10.22146/.v9i3.382

WS Lima, E. Souto, K. El-Khatib, R. Jalali, and J. Gama, “Human activity recognition using inertial sensors in a smartphone: An overview,” Sensors (Switzerland), vol. 19, no. 14, pp. 14–16, 2019. https://doi.org/10.3390/s19143213

N. Cruz Silva, J. Mendes-Moreira, and P. Menezes, “Features Selection for Human Activity Recognition with iPhone Inertial Sensors,” Adv. Arti. Intell. 16th Port. conf. Arti. Intel., no. September, pp. 560–570, 2013.

IA Bustoni, I. Hidayatulloh, AM Ningtyas, A. Purwaningsih, and SN Azhari, “Classification methods performance on human activity recognition,” J. Phys. conf. Ser., vol. 1456, no. 1, 2020. https://doi.org/10.1088/1742-6596/1456/1/012027

YA Kurniawan, " Klasifikasi Static Dan Dynamic Activity Pada Human Activity Recognition Dataset Menggunakan Convolutional Neural Network," Universitas Muhammadiyah Malang, 2020.

M. Ullah, H. Ullah, SD Khan, and FA Cheikh, “Stacked Lstm Network for Human Activity Recognition Using Smartphone Data,” Proc. - Euros. Work. vis. inf. Process. EUVIP, vol. 2019-Octob, pp. 175–180, 2019. https://doi.org/10.1109/EUVIP47703.2019.8946180

R. Mutegeki and DS Han, “A CNN-LSTM Approach to Human Activity Recognition,” 2020 Int. conf. Arti. Intell. inf. comm. ICAIIC 2020, pp. 362–366, 2020. https://doi.org/10.1109/ICAIIC48513.2020.9065078

A. Dhillon and GK Verma, “Convolutional neural network: a review of models, methodologies and applications to object detection,” prog. Arti. Intell., vol. 9, no. 2, pp. 85–112, 2020. https://doi.org/10.1007/s13748-019-00203-0

MR Alwanda, PRR Kurniawan, and D. Alamsyah, “Implementasi Metode Convolutional Neural Network Menggunakan Arsitektur LeNet-5 untuk Pengenalan Doodle,” vol. 1, no. 1, 2020. https://doi.org/10.35957/algoritme.v1i1.434

L. Alawneh, B. Mohsen, M. Al-Zinati, A. Shatnawi, and M. Al-Ayyoub, “A Comparison of Unidirectional and Bidirectional LSTM Networks for Human Activity Recognition,” 2020 IEEE Int. conf. Pervasive Computing. comm. Work. PerCom Work. 2020, 2020. https://doi.org/10.1109/PerComWorkshops48775.2020.9156264

A. Rahmawati, "Klasifikasi Tumor Otak Menggunakan Convolutional Neural Network," 2020.

A. Bimantara and TA Dina, "Klasifikasi Web Berbahaya Menggunakan Metode Logistic Regression," Annu. res. Semin., vol. 4, no. 1, pp. 173–177, 2019.

Issue

Vol. 6, No. 2, May 2021

Classification of Human Activity Recognition Utilizing Smartphone Data of CNN-LSTM

Corresponding Author(s) : Yuda Munarko

Abstract

Keywords

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