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Human Activity Recognition with Smartphones Using Machine Learning

Year: 2024

Type: article

Abstract: Human activity recognition is widely used now in many applications, such as smart homes, health care, and business as well as in a wide range of pattern recognition and human-computer interaction research. In this paper, we use the same sensors embedded in smartphones (Accelerometer and Gyroscope) to track and recognize human activities. We employ a machine-learning algorithm, which is the Support Vector Machine (SVM) to improve the performance of human activity recognition. The experimental results on the HAR Dataset from the UCI repository indicate that our approach is practical and achieves 96% accuracy.

Source: Journal of King Abdulaziz University-Computing and Information Technology Sciences

DOI: 10.4197/comp.13-2.6

Authors: Wejdan Alghamdi, Ameerah Alshahrani, Nehal Otaif, Najlaa Alqurashi

Institution: King Abdulaziz University

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4405978692

FWCI:

Cites: 20

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Related to: 10