Human Activities Recognition Using Machine Learning and Artificial Initialization

Vishva Gandhi

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 2, 520-524

Abstract: Human Activity Recognition (HAR)is an important challenge for applications in various areas such as healthcare, smart environments, and surveillance. In this paper, we propose a machine learning and artificial intelligence-based approach for HAR using wearable sensor data. The proliferation of wearable devices has made it possible to collect a wide range of sensor data, including accelerometer and gyroscope readings, providing valuable insights into human activity. Our proposed approach uses machine learning algorithms, including support vector machines (SVMs), random forests, and artificial neural networks (ANNs), to classify human activities based on sensor data. We explore feature extraction methods that transform raw sensor readings into meaningful representations including time- and frequency-domain features. We also explore the effectiveness of feature selection methods to identify the most discriminatory features for activity recognition. We also use deeplearning techniques suchas Convolutional Neural Network (CNN)and Recurrent Neural Network (RNN) to automatically learn hierarchical representations from sensor for HAR.We are developing a deeplearning architecture tailored to sequential sensor data that captures both the spatial and temporal dependencies inherent in human activity. We evaluate the proposed approach on publicly available datasets covering a variety of human activities, including walking, running, sitting, standing, and other common daily activities. Experimental results demonstrate the effectiveness of our method in accurately recognizing human activities, outperforming baseline approaches, and achieving state-of-the-art performance on HAR tasks. We also compare and analyses various machine learning and deep learning models to review the pros and cons of HAR applications. We also discuss practical considerations such as computational complexity, scalability, and real-time performance, highlighting challenges and opportunities for future research.

Keywords: Machine Learning; Support Vector Machines; Random Forests; Artificial Neural Networks; Convolutional Neural Network; Recurrent Neural Network (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2410276
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/home/article/view/CSEIT2410276 Article URL (text/html)
https://ijsrcseit.com/home/article/download/CSEIT2410276/CSEIT2410276 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i2:id:95

DOI: 10.32628/CSEIT2410276

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma (USA) ().