A review of wearable sensors based fall-related recognition systems. (May 2023)

• Record Type: Journal Article
• Authors: Liu, Jiawei; Li, Xiaohu; Huang, Shanshan; Chao, Rui; Cao, Zhidong; Wang, Shu; Wang, Aiguo; Liu, Li
• Is Part Of: Engineering applications of artificial intelligence. Volume 121(2023)
• Publication Date: 2023-05
• Subjects: Elderly falls -- Wearable sensors -- Fall prediction -- Fall detection -- Fall classification; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2023.105993
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Falls are an important factor in significantly deteriorating quality of life of older adults, consequently leading to both physical and psychological harm. A wearable-based fall-related recognition system (WFRS) indeed facilitates the prediction, detection, and classification of fall events in helping fallers. Previous studies have provided a relatively comprehensive introduction to WFRSs from the perspective of sensor types and recognition algorithms. However, while these studies provide a clear technical direction, how to choose the appropriate technology for each phase of the experiment is a stumbling block for newly interested researchers. Accordingly, a comprehensive review article covering the mainstream technologies of WFRSs is imperative and meaningful. This review analyzes 48 state-of-the-art researches in WFRSs from three databases (i.e., IEEE Explorer, ScienceDirect, and MDPI) and introduces the pipeline techniques that consist of data acquisition, preprocessing, feature extraction, model training, and evaluation. Specifically, we first analyze the pros and cons of the use of different number of sensors for data collection. We then introduce the widely used preprocessing techniques including filtering and data augmentation. Afterwards, we detail the extraction of various features and illustrate methods for the selection, training, and evaluation of fall recognition models. We finally discuss factors affecting the overall performance of a model and offer suggestions for future research. Highlights: A comprehensive review of Wearable-based Fall-related Recognition Systems. Summarize and discuss the techniques of data acquisition, preprocessing and augmentation in FRSs. Investigate FRSs research that implemented threshold, machine learning and deep learning algorithms. Discuss factors determining WFRSs' overall performance and show future directions.