A Study of Fall Detection in Assisted Living: Identifying and Improving the Optimal Machine Learning Method

Abstract

This paper makes four scientific contributions to the field of fall detection in the elderly to contribute to their assisted living in the future of internet of things (IoT)-based pervasive living environments, such as smart homes. First, it presents and discusses a comprehensive comparative study, where 19 different machine learning methods were used to develop fall detection systems, to deduce the optimal machine learning method for the development of such systems. This study was conducted on two different datasets, and the results show that out of all the machine learning methods, the k-NN classifier is best suited for the development of fall detection systems in terms of performance accuracy. Second, it presents a framework that overcomes the limitations of binary classifier-based fall detection systems by being able to detect falls and fall-like motions. Third, to increase the trust and reliance on fall detection systems, it introduces a novel methodology based on the usage of k-folds cross-validation and the AdaBoost algorithm that improves the performance accuracy of the k-NN classifier-based fall detection system to the extent that it outperforms all similar works in this field. This approach achieved performance accuracies of 99.87% and 99.66%, respectively, when evaluated on the two datasets. Finally, the proposed approach is also highly accurate in detecting the activity of standing up from a lying position to infer whether a fall was followed by a long lie, which can cause minor to major health-related concerns. The above contributions address multiple research challenges in the field of fall detection, that we identified after conducting a comprehensive review of related works, which is also presented in this paper.

A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof of Concept

“…These respective functionalities for fall detection and indoor localization are outlined in 3.1 and 3.2, respectively. The proposed design and the associated system specifications that integrate both these functionalities [84,85] as a software solution for a real-world environment are presented in Section 3.3.…”

Country-Specific Interests towards Fall Detection from 2004–2021: An Open Access Dataset and Research Questions

“…Therefore, we surveyed a total of 59 works related to fall detection, out of which the first 30 works have focused on presenting and discussing the needs and interests related to fall detection in different countries of the world and the remaining 29 works are recent approaches which focused on various advancements in fall detection research. These recent approaches for fall detection have explored the intersections of multiple disciplines and may broadly be classified into three generations-Generation 1, Generation 2, and Generation 3, based on their functionalities, applicability, operational characteristics, performance, and user acceptance, as presented in the comprehensive review of fall detection-related works in [13].…”

Performance of Distribution Regression with Doubling Measure under the seek of Closest Point

“…Therefore, there has been suggested reproducing Kernel Hilbert Space (RKHS) [10] Later the importance of RKHS has been well studied in a future work by Christmann and Steinwart [13]. In a series of works Thakur and Han [16], [17] showed the application of kNN to different machine learning problems. kNN has been studied well studied in nonparametric regression previously by Gyorfi et al in [12] and it has been shown that as the number of data-points increases, the model parameter k also should grow to ensure consistency.…”