# Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey
## 2 Related Work  
Reviewing fall detection has already been studied by several researchers. [PERSON] et al. [21] conducted a survey of fall detection systems in the context of elderly care, covering algorithms based on wearable, audio, and video technologies. They deduced that the video-based system is the best due to its versatility. However, they did not explore the fall detection system based on the Kinect technology. [PERSON] et al. [25] surveyed a variety of fall detection algorithms for monitoring the health of the elderly. First, they analyzed fall detection based on the acceleration sensor. After a comparison, they concluded that using a single accelerometer is limited, as the false-positive judgment increases.  
Moreover, the video-based fall detection system is highly accurate [21]. [PERSON] et al. [26] reviewed the application of the Kinect technology in three application domains of healthcare: physical therapy and rehabilitation, clinical environments, and fall detection. For the fall detection subsection, they outlined ten studies based on the Kinect V1 relatively to their specific application, i.e., fall detection or fall prevention. In addition, they identified  
Figure 1: Types of sensors deployed in fall detection, with \(+\) indicating advantages and \(-\) indicating disadvantages.  
the main drawbacks of using Kinect in healthcare (occlusion, self-occlusion, and unconventional body postures). [PERSON] et al. [27] surveyed only vision-based methods for fall detection. They divided the vision-based fall detectors into three classifications according to the camera (single RGB, multiple RGB, and depth). [PERSON] et al. [28] reviewed three types of applications related to falls (fall detection, fallers classification, and fall risk) using wearable sensors. The selected paper's fall risk screening was the most carefully considered study. The authors identified essential perspectives based on sensor type, sample rate, signal processing, and sample size.  
This article presents an exhaustive view of the component modules of a fall detection approach based on Kinect. It presents guidelines for further research in the area of automatic fall detection. The following contributions make this survey different from previous surveys or reviews related to fall detection:  
* This article serves as a valuable reference for the development of elderly fall detection applications. We have compiled a comprehensive overview of both older and recent articles spanning from 2011 to 2022. Additionally, we provide in-depth insights into the various components of fall detection approaches, including data acquisition, pre-processing, feature extraction, actuation algorithms, and alert systems. Furthermore, we offer an extensive examination of performance metrics and highlight all publicly available Kinect-based datasets, making this resource indispensable for researchers and practitioners in the field.
* We cover Kinect applications in a critical area of our society, namely, healthcare fall detection.
* We do not focus on commercial fall detection products.
* We present guidelines for further research.