Abstract
Human fall due to an accident can cause heavy injuries which may lead to a major medical issue for elderly people. With the introduction of new advanced technologies in the healthcare sector, an alarm system can be developed to detect a human fall. This paper summarizes various human fall detection methods and techniques, through observing people’s daily routine activities. A human fall detection system can be designed using one of these technologies: wearable based device, context-aware based and vision based methods. In this paper, we discuss different machine learning models designed to detect human fall using these techniques. These models have already been designed to discriminate fall from activities of daily living (ADL) like walking, moving, sitting, standing, lying and bending. This paper is aimed at analyzing the effectiveness of these machine learning algorithms for the detection of human fall.
Highlights
Human falls are considered as dreaded events since they can affect a person physically as well as psychologically
As output features like shock response spectrum and Mel Frequency Cepstral Coefficients (MFCC) were calculated and forwarded to Bayes classifier to differentiate between a fall and an activities of daily living (ADL)
This paper provides a literature review of different approaches used for the development of an automatic human fall detection system
Summary
Human falls are considered as dreaded events since they can affect a person physically as well as psychologically. One of the main advantages of these systems is that they are so intelligent that they communicate with the emergency contact immediately whenever a fall is detected even in the cases where the patient is unconscious or unable to get up after the fall (Wang et al, 2008; Sposaro and Tyson, 2009; Hijaz et al, 2010; de Miguel et al, 2017). These systems increase the independent living ability of elderly people i.e. elderly people who previously, were dependent on another human being, don’t require any manual support in terms of the presence of another person all the time. Precision is calculated as the total number of correctly detected falls divided by the total number of falls detected
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