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.

中文翻译：

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跌倒检测系统机器学习的系统综述

跌倒是对老年人健康和生命的重大威胁。跌倒检测系统（FDS）通过识别跌倒并救助他们的生命来帮助长者。基于机器学习（ML）的FDS由于能够自动帮助老年人而成为了一个主要的研究领域。FDS的效率取决于其准确识别非跌落的能力。最初的跌倒检测方案取决于基于阈值的分类，以根据“日常生活活动”（ADL）对跌倒进行分类，但是这种分类方法未能降低误报率，这引发了对该技术效率的质疑。这项检查工作从现有工作中识别了基于阈值的分类中的问题，并发现了需要一种有效的基于ML的分类技术来准确识别跌倒的方法。然后，提出了关于跌倒检测中各种基于ML的分类的综合文献综述。此外，审查还调查了与基于ML的技术相关的缺点，以供将来研究之用。这项研究发现，尽管基于ML的技术远远优于基于阈值的技术，但目前基于ML的FDS尚未解决诸如数据预处理和数据降维技术之类的问题。研究得出结论，基于自适应的FDS以及基于ML的模型的复杂性降低应集中在未来的研究中。这项研究发现，尽管基于ML的技术远远优于基于阈值的技术，但目前基于ML的FDS尚未解决诸如数据预处理和数据降维技术之类的问题。研究得出结论，基于自适应的FDS以及基于ML的模型的复杂性降低应集中在未来的研究中。这项研究发现，尽管基于ML的技术远远优于基于阈值的技术，但目前基于ML的FDS尚未解决诸如数据预处理和数据降维技术之类的问题。研究得出结论，基于自适应的FDS以及基于ML的模型的复杂性降低应集中在未来的研究中。