A systematic review on machine learning for fall detection system. (5th April 2021)

• Record Type: Journal Article
• Authors: Rastogi, Shikha; Singh, Jaspreet
• Other Names: Li Ying guestEditor.; Shyamasundar R.K. guestEditor.; Wang Xinheng guestEditor.
• Is Part Of: Computational intelligence. Volume 37:Number 2(2021)
• Page Start: 951
• Page End: 974
• Publication Date: 2021-04-05
• Subjects: fall detection -- false alarm rate -- health care -- machine learning -- threshold based classification; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12441
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Fall is a major threat to the health and life of the elders. A Fall Detection System (FDS) assist the elders by identifying the fall and save their life. Machine Learning‐ (ML) based FDS has turned into a major research area due to its capability to assist the elders automatically. The efficiency of a FDS depends on its strength to identify the fall from nonfall accurately. The initial fall detection scheme depends on the threshold‐based classification to classify the fall from the Activity of Daily Living (ADL) but this classification method has failed to reduce the false alarm rate, which raises a question on the efficiency of the technique. This review work identifies the problems in threshold‐based classification from existing works and finds the need for an efficient ML‐based classification technique to accurately identify the fall. Then, presents a comprehensive literature review on various ML‐based classification in fall detection. Moreover, the scrutiny investigates the shortcomings associated with the ML‐based techniques for future research. This study finds that present ML‐based FDS has not addressed problems like data preprocessing and data dimensionality reduction techniques even though ML‐based techniques are far superior to threshold‐based techniques. The study concludes that Self‐Adaptive‐based FDS, as well as the complexity reduction of ML‐based models, should be concentrated in future research.