## 3 Fall Detection Methods and Technologies
### Fall Detection Sensor Types
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Authors** & **Year** & **Methods** \\ \hline [PERSON] et al. [26] & 2010 & Categorized methods into vision based, ambient-sensor based, and kinematic-sensor based approaches. \\ \hline [PERSON] et al. [27] & 2013 & Three main classes, namely wearable devices based, ambience device based, and vision based. \\ \hline [PERSON] and [PERSON] [28] & 2014 & Categorized methods into wearable devices and external sensors that includes vision based and ambient sensors. \\ \hline [PERSON] et al. [30] & 2015 & Utilizing vision-based methods with RGB and 3D depth cameras. \\ \hline [PERSON] et al. [32] & 2013 & Categorized into context-aware systems and wearable devices including smartphones. \\ \hline [PERSON] et al. [33] & 2012 & Dividing the applied technologies based on manually operated devices, body worn automatic alarm systems and devices that sense the high risk of falling. \\ \hline [PERSON] et al. [35] & 2015 & Systematic classification and comparison of various proposed detection algorithms with respect to the system architecture, sensors, methods and false alarm cases. \\ \hline [PERSON] and [PERSON] [36] & 2016 & Two high level categories have been defined: sufficient training data for falls or insufficient (or no training) data for falls, without considering sensor types and/or feature extraction/selection methodologies. \\ \hline [PERSON] et al. [37] & 2019 & Split methods based on the features that are used to feed the neural network classifiers into three categories: (1) Raw data obtained, (2) Features derived from the sensor readings, and (3) Combination of the raw data and other derived features. \\ \hline [PERSON] et al. [39] & 2022 & Using IoT framework, human fall classification was achieved by using ANNs model and a mathematical model of the Morlet wavelet, based on measurements of two accelerometers, a microphone and a doppler sensor. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._changes is known as photoplethysmography. Spirometers are diagnostic tools that measure lung capacity and airflow. Galvanic skin response is a component of the human sympathetic nervous system that reveals electrical features of the skin. Blood pressure is measured in systolic and diastolic units, with systolic being the highest and diastolic being the lowest. Furthermore, sensors can track the blood's oxygen saturation. Electrooculography is a technique that uses the retina's resting potential to quantify eye movement. Physiological signals can be easily recognized, such as body temperature, or they can be detected more complexly, as indicated above. A system of smart environmental sensors is used in smart environments to detect falls by voice, light intensity changes, and distance between the body and the floor. In [45], this study proposed radio signals of Wi-Fi connections for detecting falls from the physical components in the environment. In addition, environmental sensors are categorized as vision-based sensors. A fall event can be identified by analyzing RGB images and video streams from 2D and 3D cameras [46; 47; 48; 49], based on color and/or thermal postures processing. Sensor positioning is still an important topic, as mentioned in previous research [50]. For body position [51], body area sensors are placed in a predetermined area such as the chest, hands, and pocket of a shirt. Environmental positions for sensor placement include the bed, the floor, the walls, and event items such as a tap or a handhold. Some integrated sensors can be embedded in platforms like smartphones [52; 53; 54; 55; 56; 57] or wristwatches [58; 59]. Wi-Fi, Bluetooth [60; 61; 62], ZigBee [63; 64], and other communication channels [65; 66] are used to connect with the sensors.