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All Journal International Journal of Evaluation and Research in Education (IJERE) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOIN (Jurnal Online Informatika) AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika J-SAKTI (Jurnal Sains Komputer dan Informatika) eProceedings of Engineering Jurnal INFOTEL Jurnal Informatika: Jurnal Pengembangan IT Intechno Journal : Information Technology Journal LOGIC: Jurnal Penelitian Informatika
Nuha, Hilal H
Fakultas Informatika, Universitas Telkom
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Social Sciences

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HUMAN ACTIVITY RECOGNITION IMPROVEMENT ON SMARTPHONE ACCELEROMETERS USING CIMA Putrada, Aji Gautama; Abdurohman, Maman; Perdana, Doan; Nuha, Hilal Hudan
TEKTRIKA Vol 8 No 2 (2023): TEKTRIKA Vol.8 No.2 2023
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v8i2.6973

Abstract

Human activity recognition (HAR) is a research field that focuses on detecting user activities and has wide applications. However, the problems that need to be solved are real-time constraints and imbalanced datasets due to different activity frequencies. Our research aims to apply classification integrated moving averages (CIMA) to HAR by evaluating its performance regarding real-time constraints and imbalanced datasets. We achieved the smartphone accelerometer dataset from Kaggle, which consists of several activities: walking, jogging, climbing, and descending stairs. We develop a general CIMA windowing algorithm with hyperparameters J and W. We benchmark CIMA with two state-of-the-art HAR methods: distributed online activity recognition system (DOLARS) and convolutional neural network (CNN). We conducted some imbalance and model size analysis. The test results show that, with J = 10 and W = 240, CIMA performs better than DOLARS and CIMA with recall, precision, and f1-score of 0.996, 0.993, and 0.994. We also prove that CIMA, assisted by quantization, has the smallest model size compared to the CNN and DOLARS model sizes. Finally, we demonstrate that CIMA performs well for imbalanced datasets, where CIMA’s recall on upstairs and downstairs activities is better than DOLARS and CNN, with values of 1.00 and 0.98, respectively. Key Words: classification integrated moving average, human activity recognition, smartphone, accelerometer, imbalanced dataset
Co-Authors Absa, Ahmad Abo Adin, Nur Adrian, Muhammad Kholilul Aji Gautama Putrada AULIA ARIF WARDANA Danuwijaya, Edghar Dita Oktaria Doan Perdana Endro Ariyanto Fajrin, Alvin Nur Favian Dewanta Ikke Dian Oktaviani Maisyadiva, Rafli Maman Abdurohman Maslin Masrom Mufidah, Nada Fajri Muhammad Al Makky Naufal, Febryanza Novian Anggis Suwastika Nurhalim, Rifqy Parman Sukarno Prajna Deshanta Ibnugraha Putra, Yohanes Armenian Qonita, Qori Rahmat Yasirandi Rio Guntur Utomo Salsabila, Balqis Hasna Suryahadi, Daniel Putra