Browsing by Person "Ahmed, Mustafa Elhadi"

Now showing 1 - 2 of 2

Advancing real-world applications: A scoping review on emerging wearable technologies for recognizing activities of daily living
(Elsevier, 2025-03-25) Ahmed, Mustafa Elhadi; Yu, Hongnian; Vassallo, Michael; Koufaki, Pelagia
Wearable technologies for Activities of Daily Living (ADL) recognition have emerged as a crucial area of research, driven by the global rise in aging populations and the increase in chronic diseases. These technologies offer significant benefits for healthcare by enabling continuous monitoring and early detection of health issues. However, the field of ADL recognition with wearables remains under-explored in key areas such as user variability and data acquisition methodologies. This review aims to provide a comprehensive overview of recent advancements in ADL recognition using wearable devices, with a particular focus on commercially available devices. We systematically analyzed 157 studies from six databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, narrowing our focus to 77 articles that utilized proprietary datasets. These studies revealed three main categories of wearables: prototype devices (40 %), commercial research-grade devices (32 %), and consumer-grade devices (28 %) adapted for ADL recognition. Additionally, various detection algorithms were identified, with 31 % of studies utilizing basic machine learning techniques, 40 % employing advanced deep learning methods, and the remainder exploring ensemble learning and transfer learning approaches. Our findings underscore the growing adoption of accessible, commercial devices for both research and clinical applications. Furthermore, we identified two key areas for future research: the development of user-centered data preparation techniques to account for variability in ADL performance, and the enhancement of wearable technologies to better align with the practical needs of healthcare systems. These advancements are expected to enhance the usability and efficiency of wearables in improving patient care and healthcare management.
Enhanced LDA with SVM and Ensemble Learning for Advanced ADL Classification Using Smartwatch Data
(IEEE, 2024-08-28) Ahmed, Mustafa Elhadi; Yu, Hongnian; Vassallo, Michael; Koufaki, Pelagia
The demographic shift towards an older population introduces significant challenges for healthcare, necessitating advancements in predictive, dynamic care models. Central to this transformation are Activities of Daily Living (ADLs) and Instrumental Activities of Daily Living (IADLs), which are crucial for evaluating an individual's functional status. This study leverages Machine Learning (ML) with smartwatch technology for continuous, non-intrusive monitoring of ADLs /IADLs. By integrating Support Vector Machine (SVM) and Ensemble Learning (EL) with enhanced Linear Discriminant Analysis (LDA), we aim to accurately classify a broad spectrum of daily activities. Data from 30 participants, captured through a smartwatch, underwent analysis to assess these methodologies' efficacy in real-life applications. Results indicate that LDA enhanced classification accuracy, with EL models demonstrating superior performance in recognizing complex activities—achieving over 90% accuracy. The study also examines the hardware performance of these models, noting variations in RAM usage, inference times, CPU usage, and network sizes. EL models, especially when coupled with LDA, offer an optimal balance between computational demands and classification precision. This investigation underlines the potential of integrating sophisticated data processing techniques with wearable technologies for health monitoring, presenting a viable approach to overcoming real-life deployment challenges and advancing personalized healthcare for the aging demographic.