## 3 Fall Detection Methods and Technologies  
### Taxonomy of Current Methods  
This section presents the different methods and technologies of fall detection systems in previous studies as illustrated in Table 1. [PERSON] et al. [24] place a strong emphasis on the mechanics of a fall, as well as the methods for detecting it and the evaluation criteria which rely on statistical analysis. They look at a variety of analytical methods for detecting falls, including thresholds on sensor reading velocity, no movements detection, and the sudden polarity inversion of the acceleration vector. [PERSON] [5] focuses on identifying the methods and basics of fall detection for old people. In this survey, fall detection methods are divided into three categories based on the type of fall detector: wearable device, ambient device, or vision based. The classification is further refined based on the type of analysis into motion, posture, proximity, body shape, and 3D head movement. [PERSON] et al. [25] classified real-time approaches into three categories: techniques that solely measure acceleration, techniques that integrate acceleration with other gyroscope data and static orientation to acquire fall events. [PERSON] et al. [26] presented the techniques used for detecting falls and irregular movements of the elderly by monitoring their daily activities. In this research, the surveyed techniques are classified into three categories: based on video analysis, based on acoustic and ambient sensors, and based on kinematic sensors. [PERSON] et al. [27] emphasized in their review on different fall detection systems and their underlying algorithms, that fall detection methods are divided into three classes: wearable device-based, ambiance device-based, and camera-based. These classes are then refined into smaller ones based on the type of accelerometry, posture analysis, audio/visual ambiance sensing, shape modeling, and others. [PERSON] and [PERSON] [28] conducted a survey on fall detection and prevention systems and provide a qualitative comparison among them. They categorized the sensors into two types, namely wearable and external sensor devices. The external sensors are further split into vision-based or ambient-based systems. They also list the main aspects of machine learning algorithms, e.g., feature extraction, construction, and selection. In addition, they compared the time complexities of various classification algorithms, which are decision trees, K-Nearest Neighbor, and SVM, and discussed model evaluation strategies. Some major design concerns for fall detection and prevention systems, such as privacy, cost, energy consumption, obstructiveness, and others, are also highlighted. A discussion of the possible physical, psychological, and environmental risk factors that may lead to a fall is presented. [PERSON] et al. [29] perform a systematic review of wearable-sensor-based fall detection systems. They look at whether previous research on fall detection used artificially recorded falls in a lab setting or spontaneous falls in real-life scenarios. [PERSON] et al. [30] look at research publications that employ vision sensors only. They present some publicly available datasets on fall detection. They classified vision-based approaches that use one or more RGB cameras, as well as 3D depth cameras. Automatic fall detection systems were the main focus of the survey given by [PERSON] et al. [31]. In this study, the authors categorized the current platforms into two classes: wearable and ambientdevices. Furthermore, they divided the classification techniques into two sub-categories: rule-based and machine learning-based algorithms. Other topics of fall detection systems were also covered, including sensor types and position, subject information, classification algorithms, and performance evaluation. Furthermore, they evaluated different fall detection products in terms of size, weight and type of sensors and battery with their operational functions; and then make predictions about future developments in the field. [PERSON] et al.