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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Wooi Haw Tan
Multimedia University
Computer Science & IT Electrical & Electronics Engineering

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LoRa driven medical adherence system Suheib Sherif; Wooi Haw Tan; Chee Pun Ooi; Abubaker Sherif; Sarina Mansor
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2195

Abstract

Recent discovered technologies have exposed many new theories and possibilities to improve our standard of living. Medical assistance has been a major research topic in the past, many efforts were put in to simplify the process of following treatment prescriptions. This paper summarizes the work done in developing LoRa driven medical adherence system in order to improve medicine adherence for elderlies. The designed system is composed of two sections; embedded hardware device for the use of patients at home and Web application to manage all patients along with their medicines and keep track of their medicine intake history. LoRa wireless communication technology is used for connecting all embedded devices with a central gateway that manages the network. Hardware and software tests have been conducted and showed great performance in terms of LoRa network range and latency. In short, the proposed system shows promising method of improving medicine adherence. 
A wearable device for machine learning based elderly's activity tracking and indoor location system Nour Eddin Tabbakha; Chee Pun Ooi; Wooi Haw Tan; Yi-Fei Tan
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2737

Abstract

The number of older people is increasing in many countries. By 2030, it is estimated that 15% of the overall population will be comprised of people aged 65 and above. Hence, the monitoring and tracking of elder activities to ensure they live an active life has become a major research topic in recent years. In this work, an elderly sub-activity tracking system is developed to detect the sub-activity of the elderly based on their physical activities and indoor location. The physical activities tracking system and indoor location system is combined in this project to enhance the context of the elderly activities (i.e. sub-activities as defined in this project). An indoor location system is developed by using Bluetooth Low Energy (BLE) beacon and BLE scanners to measure the Received Signal Strength Indicator (RSSI) signal to detect the location of the elderly. The activity tracking is carried out via a waist wearable device worn by the elderly. Random forest and Support Vector Machine (SVM) are used as machine learning classifiers to predict the activity and indoor location with an accuracy of 95.03% and 86.58%, respectively. The data from activity tracking and indoor location sub-systems will then be combined to derive the sub-activity and push to an online Internet of Things (IoT) platform for remote monitoring and notification.
Facial emotion recognition using deep learning detector and classifier Ng Chin Kit; Chee-Pun Ooi; Wooi Haw Tan; Yi-Fei Tan; Soon-Nyean Cheong
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3375-3383

Abstract

Numerous research works have been put forward over the years to advance the field of facial expression recognition which until today, is still considered a challenging task. The selection of image color space and the use of facial alignment as preprocessing steps may collectively pose a significant impact on the accuracy and computational cost of facial emotion recognition, which is crucial to optimize the speed-accuracy trade-off. This paper proposed a deep learning-based facial emotion recognition pipeline that can be used to predict the emotion of detected face regions in video sequences. Five well-known state-of-the-art convolutional neural network architectures are used for training the emotion classifier to identify the network architecture which gives the best speed-accuracy trade-off. Two distinct facial emotion training datasets are prepared to investigate the effect of image color space and facial alignment on the performance of facial emotion recognition. Experimental results show that training a facial expression recognition model with grayscale-aligned facial images is preferable as it offers better recognition rates with lower detection latency. The lightweight MobileNet_v1 is identified as the best-performing model with WM=0.75 and RM=160 as its hyper-parameters, achieving an overall accuracy of 86.42% on the testing video dataset.
Co-Authors Abubaker Sherif Chee Pun Ooi Chee-Pun Ooi Ng Chin Kit Nour Eddin Tabbakha Sarina Mansor Soon-Nyean Cheong Suheib Sherif Yi-Fei Tan