Advancing real-world applications: A scoping review on emerging wearable technologies for recognizing activities of daily living

Abstract

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.