## 3 Fall Detection Methods and Technologies
### Taxonomy of Current Methods
[32] examined 327 research publications on fall detection, and divided them into two main systems, namely context-aware systems and wearable devices including smartphones. The context-aware systems are classified based on system components such as cameras, pressure and infrared sensors, and microphones. [PERSON] et al. [33] analyzed relevant techniques based on the usage and implementation technology for detecting fall events, warning end-users such as relatives of patients and healthcare workers for assistance. They divide fall detection technology into three categories: manually controlled devices, body-worn automated alarm systems, and devices that detect changes that might lead to a fall. [PERSON] et al. [34] presented a comparative study and major characteristics of developed Android-based systems of fall detection. They argue that most smartphone fall detection solutions rely on machine learning (pattern matching) or preset thresholds. [PERSON] et al. [35] explored fall detection methods for Android-based smartphones. They highlighted and compared various algorithms in previous studies, considering many characteristics, e.g., proposed system design, applied sensors, methods and reaction in the event of a false alarm. From a data availability standpoint, [PERSON] and [PERSON] [36] conducted a review of fall detection approaches. They divided these approaches into two high-level classes based on whether the available training data for falls are sufficient or not, not considering the type of sensors employed or feature extraction or selection algorithms. [PERSON] et al. [37] presented a review paper on applying artificial neural networks (ANNs) in fall detection systems that rely on wearable sensors. The authors of this study split fall detection methods into three categories based on the features that are given to the ANN-based classifiers. The first category includes features based on the raw data from the sensor measurement. The second category includes fall detection features derived from the pre-processing measurements. The last category contains fall features based on a combination of raw sensor data and other features calculated from the sensor readings. The authors of [38] conducted a comprehensive review of all papers, projects, and patents on the topic of fall prediction, detection, and prevention from around the world. The review divided the relevant works into categories depending on the technique they employed, their types, and their accomplishments. The approach proposed by [PERSON] et al. [39] is based on IoT devices deployed in people's homes. The suggested non-wearable solution is non-intrusive and can be used in a variety of settings, including residences, hospitals, rehabilitation centers, and homes for the elderly. Edge, fog, and cloud are all part of the solution's three-layer processing architecture. For human fall classification, an artificial intelligence model based on ANNs and a mathematical model based on the Morlet wavelet are both employed and contrasted. The results demonstrated that combining both models is practical and beneficial to the system, with a 92.5% accuracy and no false negatives. \begin{table}
\begin{tabular}{c c c} \hline
**Authors** & **Year** & **Methods** \\ \hline [PERSON] et al. [24] & 2007 & Using analytical methods with thresholds of accelerometer sensor readings. \\ \hline [PERSON] [5] & 2008 & Categorized methods based on wearable, computer vision and ambient devices. \\ \hline [PERSON] et al. [25] & 2009 & Using techniques that measure only the acceleration or combined with gyroscope and static orientation data for fall detection. \\ \hline \end{tabular}
\end{table} 
Table 1: Taxonomy of main fall detection methods with different sensing devices.