A5041915757- Context-Aware Activity Recognition Systems
- Anomaly Detection Techniques and Applications
- Smoking Behavior and Cessation
- Human Pose and Action Recognition
- Child Development and Digital Technology
- Social Media and Politics
- Impact of Technology on Adolescents
- Time Series Analysis and Forecasting
- Mobile Health and mHealth Applications
- Insurance, Mortality, Demography, Risk Management
- Imbalanced Data Classification Techniques
Majmaah University
2022-2025
Umm al-Qura University
2025
Goldsmiths University of London
2018-2022
Human activity recognition (HAR) based on wearable sensors has emerged as an active topic of research in machine learning and human behavior analysis because its applications several fields, including health, security surveillance, remote monitoring. Machine algorithms are frequently applied HAR systems to learn from labeled sensor data. The effectiveness these generally relies having access lots accurately training But data for is hard come by often heavily imbalanced favor one or other...
Human activity recognition (HAR) using wearable sensors is an increasingly active research topic in machine learning, aided part by the ready availability of detailed motion capture data from smartphones, fitness trackers, and smartwatches. The goal HAR to use such devices assist users their daily lives application areas as healthcare, physical therapy, fitness. One main challenges for HAR, particularly when supervised learning methods, obtaining balanced algorithm optimisation testing. As...
Smoking is linked to more than two million preventable deaths yearly. The widespread use of sensors embedded in everyday devices provides novel means for research on smoking. Smartphones and smartwatches can monitor smoking behavior, which could lead the development new methods reduction or cessation. However, often co-occurs with other activities, such as drinking eating, makes recognition concurrent overlapping activities from wearable challenging. In this paper, we proposed first time...