@inproceedings{9b78ac93f92748bf8766ef1b64fc10bf,
title = "IMU-based Human Activity Recognition using Machine Learning and Deep Learning models",
abstract = "Research into Human Activity Recognition (HAR) with wearable sensors is attracting a lot of attention due to its wide range of applications. In this paper, we use a microcontroller with an integrated Inertial Measurement Unit (IMU) to design various Machine Learning (ML) and Deep Learning (DL) models. Five different activities were captured: walking, walking up a staircase, walking down a staircase, jumping, and falling. Both ML and DL models were trained on a variety of IMU data. A comparison was performed among the models based on their accuracy scores and confusion matrices to determine the most effective one. The proposed LSTM model achieved an accuracy score of 99% when trained with accelerometer and gyroscope data from the IMU.",
keywords = "Deep Learning, Human Activity Recognition, IMU Sensor, Machine Learning",
author = "Saad Alkharji and Aysha Alteneiji and Kin Poon",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 6th International Conference on Signal Processing and Information Security, ICSPIS 2023 ; Conference date: 08-11-2023 Through 09-11-2023",
year = "2023",
doi = "10.1109/ICSPIS60075.2023.10343567",
language = "British English",
series = "2023 6th International Conference on Signal Processing and Information Security, ICSPIS 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "62--66",
booktitle = "2023 6th International Conference on Signal Processing and Information Security, ICSPIS 2023",
address = "United States",
}