Pengenalan Aktivitas Manusia Menggunakan Sensor Akselerometer dan Giroskop pada Smatphone dengan Metode K-Nearest Neighbor

Penulis

Zainal Arifien, Fitra Abdurrahman Bachtiar, Novanto Yudistira

Abstrak

Pengenalan aktivitas manusia atau Human Activity Recognition (HAR) merupakan salah satu topik yang populer karena besarnya peluang untuk diterapkan di kehidupan sehari-hari. Tujuan dari pengenalan ini adalah untuk mengenali, mendeteksi, dan mengklasifikasikan aktivitas yang dilakukan manusia. Pengenalan aktivitas manusia adalah salah satu teknologi penting untuk memantau dinamisme seseorang sehingga dapat bermanfaat di berbagai hal. Selain untuk menjaga kesehatan, pencegahan penyakit, dan membantu menentukan jenis olah raga, HAR dapat dimanfaatkan juga untuk diterapkan pada bidang keamanan dan pengembangan teknologi. Penelitian ini menggunakan smartphone sebagai teknologi utama dalam memperoleh data dengan memanfaatkan sensor akselerometer dan giroskop yang telah tertanam di dalamnya. Terdapat 8 macam aktivitas yang diteliti dengan kombinasi lama waktu eksperimen 5, 10, dan 15 detik serta posisi smartphone dipegang bebas maupun di dalam saku celana kanan. Data yang diperoleh terdiri dari 3 sumbu (x, y, dan z) pada setiap sensor yang digunakan. Data tersebut kemudian melalui proses pengolahan dan klasifikasi menggunakan algoritme k-Nearest Neighbor (k-NN). Hasil akurasi yang didapat dalam penelitian ini mencapai 79,56%. Hasil yang diperoleh melalui penelitian ini menunjukkan bahwa perbedaan peletakan smartphone mempengaruhi hasil pengenalan aktivitas manusia secara stabil. Selain itu, perbedaan jumlah data akibat perbedaan lamanya waktu eksperimen dapat mengakibatkan perbedaan lamanya waktu komputasi. Penelitian ini menjadi penting karena hasilnya dapat menjadi batu loncatan bagi penelitian selanjutnya. Beberapa peluang pengembangan juga dilampirkan pada bagian akhir.

 

Abstract

Human activity recognition (HAR) is one of the most popular topics because of the large opportunities for its application in life. The purpose of HAR is to recognize, detect and classify human activities. Human activity recognition is one of the important technologies for monitoring a person's dynamism so that it can be utilized in various ways. Apart from maintaining health, preventing disease, and helping determine the type of exercise, HAR can also be used to be applied in the field of security and technological developments. This study uses smartphones as the main technology in obtaining data by utilizing the built-in accelerometer and gyroscope sensors. There are 8 types of activities studied with a combination of 5, 10, and 15 seconds of experimental time and the position of the smartphone is carried freely or in the right trouser pocket. The data obtained consists of 3 axes (x, y, and z) on each sensor used. The data then processed and classified using the k-Nearest Neighbor (k-NN) algorithm. The accuracy results obtained in this study reaches 79.56%. The results obtained through this study indicate that differences in smartphone placement affect the results of human activity recognition stably. In addition, differences in the amount of data due to differences in the length of the experiment period can result in differences in the length of computation time. This research is important because the results can be used as material for further research assistance. Some development opportunities are also attached at the end.

 

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Referensi


ANGUITA, D., GHIO, A., ONETO, L., PARRA, X. dan REYES-ORTIZ, J.L., 2013. A Public Domain Dataset for Human Activity Recognition Using Smartphones. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). [online] Bruges (Belgium).pp.437–442. Tersedia melalui: [Diakses 12 Mar. 2020].

ANON 2021. Mi Global Home. [online] Tersedia melalui: [Diakses 28 May 2021].

ANON 2021. Laboratorium Sistem Cerdas | Fakultas Ilmu Komputer (FILKOM) Universitas Brawijaya. [online] Tersedia melalui: [Diakses 23 Jul. 2021].

BAHARUDDIN, M.M., HASANUDDIN, T. dan AZIS, H., 2019. ANALISIS PERFORMA METODE K-NEAREST NEIGHBOR UNTUK IDENTIFIKASI JENIS KACA. ILKOM Jurnal Ilmiah, [online] 11(3), pp.269–274. Tersedia melalui: .

BATOOL, M., JALAL, A. dan KIM, K., 2019. Sensors Technologies for Human Activity Analysis Based on SVM Optimized by PSO Algorithm. 2019 International Conference on Applied and Engineering Mathematics (ICAEM). [online] IEEE.pp.145–150. Tersedia melalui: .

BIEBER, G., LUTHARDT, A., PETER, C. dan URBAN, B., 2011. The Hearing Trousers Pocket – Activity Recognition by Alternative Sensors. ACM International Conference Proceeding Series.

FAN, L., WANG, Z. dan WANG, H., 2013. Human Activity Recognition Model Based on Decision Tree. 2013 International Conference on Advanced Cloud and Big Data. [online] IEEE.pp.64–68. Tersedia melalui: .

GARCIA-GONZALEZ, D., RIVERO, D., FERNANDEZ-BLANCO, E. dan LUACES, M.R., 2020. A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors. Sensors, [online] 20(8), pp.1–14. Tersedia melalui: .

GULZAR, Z., LEEMA, A.A. dan MALASERENE, I., 2019. Human Activity Analysis using Machine Learning Classification Techniques. International Journal of Innovative Technology and Exploring Engineering, [online] 9(2), pp.3252–3258. Tersedia melalui:

content/uploads/papers/v9i2/B7381129219.pdf>.

HA, S. dan CHOI, S., 2016. Convolutional Neural Networks for Human Activity Recognition using Multiple Accelerometer and Gyroscope Sensors. 2016 International Joint Conference on Neural Networks (IJCNN). [online] IEEE.pp.381–388. Tersedia melalui: .

https://a-har.org/, 2020. aHAR - affective-Human Activity Recognition. [online] Tersedia melalui: [Diakses 20 Sep. 2020].

HU, L.-Y., HUANG, M.-W., KE, S.-W. dan TSAI, C.-F., 2016. The distance function effect on k-nearest neighbor classification for medical datasets. SpringerPlus, [online] 5(1), p.1304. Tersedia melalui: .

JAOUEDI, N., BOUJNAH, N., HTIWICH, O. dan BOUHLEL, M.S., 2016. Human action recognition to human behavior analysis. 2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT). [online] IEEE.pp.263–266. Tersedia melalui: .

JOBANPUTRA, C., BAVISHI, J. dan DOSHI, N., 2019. Human Activity Recognition: A Survey. Procedia Computer Science, [online] 155, pp.698–703. Tersedia melalui: .

JUNITA, V. dan BACHTIAR, F.A., 2020. Klasifikasi Aktivitas Manusia menggunakan Algoritme Decision Tree C4.5 dan Information Gain untuk Seleksi Fitur. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, [online] 3(10), pp.9426–9433. Tersedia melalui: .

KAGHYAN, S. dan SARUKHANYAN, H., 2012. Activity recognition using K-nearest neighbor algorithm on smartphone with Tri-axial accelerometer. International Journal of Informatics Models and Analysis, [online] 1, pp.146–156. Tersedia melalui: .

KHARAT, M. V, WALSE, K.H. dan DHARASKAR, R. V, 2017. Survey on Soft Computing Approaches for Human Activity Recognition. International Journal of Science and Research, [online] 6(2), pp.1328–1334. Tersedia melalui: .

ONIGA, S. dan SUTO, J., 2014. Human activity recognition using neural networks. Proceedings of the 2014 15th International Carpathian Control Conference (ICCC). [online] IEEE.pp.403–406. Tersedia melalui: .

PALIYAWAN, P., NUKOOLKIT, C. dan MONGKOLNAM, P., 2014. Prolonged sitting detection for office workers syndrome prevention using kinect. 2014 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). [online] IEEE.pp.1–6. Tersedia melalui: .

PAUL, P. dan GEORGE, T., 2015. An effective approach for human activity recognition on smartphone. 2015 IEEE International Conference on Engineering and Technology (ICETECH). [online] Coimbatore, TN, India: IEEE.pp.1–3. Tersedia melalui: .

RAVI, N., DANDEKAR, N., MYSORE, P. dan LITTMAN, M.L., 2005. Activity Recognition from Accelerometer Data. Proceedings of the Seventeenth Conference on Innovative Applications ofArtificial Intelligence. Pittsburgh, Pennsylvania, USA.pp.1541–1546.

SHAHROUDY, A., LIU, J., NG, T.-T. dan WANG, G., 2016. NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). [online] IEEE.pp.1–10. Tersedia melalui: .

USHARANI, J. dan SAKTHIVEL, U., 2016. Human Activity Recognition using Android Smartphone. International Journal of Advanced Networking & Applications, (Special Issue-1st International Conference on “Innovations in Computing & Networking” (ICICN-2016) held at RajaRajeswari College of Engineering, Bangalore), pp.191–197.

WAHYONO, W., TRISNA, I.N.P., SARIWENING, S.L., FAJAR, M. dan WIJAYANTO, D., 2020. Perbandingan penghitungan jarak pada k-nearest neighbour dalam klasifikasi data tekstual. Jurnal Teknologi dan Sistem Komputer, [online] 8(1), pp.54–58. Tersedia melalui:

www.csc.kth.se/cvap/actions/, 2020. Recognition of human actions. [online] Tersedia melalui: [Diakses 25 Nov. 2020].

ZMITRI, M., FOURATI, H. dan VUILLERME, N., 2019. Human Activities and Postures Recognition: From Inertial Measurements to Quaternion-Based Approaches. Sensors, [online] 19(19), pp.1–18. Tersedia melalui: .




DOI: http://dx.doi.org/10.25126/jtiik.2022915593