# Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey
## 7 Discussion  
The field of fall detection is pivotal in enhancing the quality of life for older adults and mitigating the risk of losing their independence. It encompasses various approaches aimed at developing and quantitatively analyzing activities. These approaches are predominantly focused on data analysis and processing, seeking to elucidate human movement through tiny sensors.  
One of the significant challenges in fall detection lies in the physical characteristics shared between falls and certain routine activities. This challenge manifests itself in the nearly indistinguishable alteration of the body's 3D information when an individual falls or is in a supine position on the ground. Another area for improvement affecting the accuracy of these techniques is the viewing angle. In order to accurately estimate the skeleton, the person must be appropriately oriented towards the Kinect device while ensuring that the right and left joints align along the depth axis (z-axis). Potential solutions include utilizing joints in the upper body or those forming the major axis of the tracked person, such as the head joint. Integrating additional features such as speed, acceleration, and inactivity time can effectively enhance the system's performance in detecting falls. Moreover, performing studies in a 2D space imposes constraints on precision and generalization, resulting in loss of information on the third axis.  
In most of the reviewed approaches, volunteers with varying characteristics (age, sex, height, weight, and health status) simulated activities within diverse environments and experimental setups. Consequently, the collected data used for method development may require greater realism. Integrating real and simulated data encompassing various fall scenarios and activities of daily living (ADLs) into a publicly accessible dataset would significantly benefit future research endeavors. This approach would facilitate equitable algorithm comparisons, streamline experimental preparations, and promote the generalization of techniques. Additionally, incorporating real-world events can better validate the effectiveness and applicability of the proposed approaches.  
Efficiently optimizing the features that represent and differentiate visual information is critical for developing dependable real-time fall detection systems. Compared to machine learning and deep learning approaches, threshold-based systems are computationally less demanding and are more suitable for 24 h surveillance systems. They exhibit lower energy consumption when contrasted with machine learning-based methods or those employing multiple sensor types. Operationally, threshold-based approaches offer rapid outputs at the cost of precision, whereas machine learning-based approaches provide high precision but may not be suitable for real-time systems due to their computational overhead. Notably, the accuracy of a fall detection approach not only depend on the type of algorithm, but on the diversity and quantity of characteristics that cover the three phases of a fall (pre-fall, critical, and post-fall)  
It is worth noting that the Kinect sensor's operational range extends up to 8 m; however, beyond 4.5 m the reliability of the acquired data diminishes, rendering skeleton detection invalid. Various filters become imperative after data acquisition to enhance the accuracy of the results. Additionally, the calibration process for the Kinect camera demands meticulous precision and time investment. This process must be repeated whenever the camera is intentionally or accidentally moved. This calibration step is obviated when employing official Kinect drivers, streamlining the operational process.