Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Thi Thu Nguyen
Hanoi University of Industry
Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Develop algorithms to determine the status of car drivers using built-in accelerometer and GBDT Thi Thu Nguyen; Phuc Thinh Doan; Anh-Ngoc Le; Kolla Bhanu Prakash; Subrata Chowdhury; Duc-Nghia Tran; Duc-Tan Tran
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp785-792

Abstract

In this paper, we introduce a mobile application called CarSafe, in which data from the acceleration sensor integrated on smartphones is exploited to come up with an efficient classification algorithm. Two statuses, "Driving" or "Not driving," are monitored in the real-time manner. It enables automatic actions to help the driver safer. Also, from these data, our software can detect the crash situation. The software will then automatically send messages with the user's location to their emergency departments for timely assistance. The application will also issue the same alert if it detects a driver of a vehicle driving too long. The algorithm's quality is assessed through an average accuracy of 96.5%, which is better than the previous work (i.e., 93%).
Real-time sleep posture classification using wearable accelerometers and machine learning models Thi Thu Nguyen; Bao Bo Quoc; Kolla Bhanu Prakash; Duc-Tan Tran
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10105

Abstract

Sleep posture plays a critical role in sleep quality and health, influencing conditions such as sleep apnea. Accurate classification of sleep postures is essential for diagnosing and treating sleep-related disorders. The sleep posture can be detected by using wearable acceleromter. This paper presents an realtime classification system for four sleep postures by integrating accelerometer data with a machine learning (ML) model. The proposed system was tested with various ML models, including decision trees (DT), random forest (RF), K-nearest neighbors (KNN), support vector classifier (SVC), and logistic regression (LR), across multiple performance metrics. The results demonstrate that the LR model, when combined with accelerometer data, significantly outperforms other methods, achieving a classification accuracy of 91%. This paper also discusses the system’s potential for real-time deployment on embedded devices, contributing to advancements in sleep posture monitoring.
Co-Authors Anh-Ngoc Le Bao Bo Quoc Duc-Nghia Tran Duc-Tan Tran Kolla Bhanu Prakash Phuc Thinh Doan Subrata Chowdhury