# New Advances and Challenges of Fall Detection Systems: A Survey
## 3 Bird's Eye View of Fall Detection Research from 2014
### Changes of Fall Detection Algorithms  
Fall detection algorithm is another deterministic factor in fall detection systems. As sensors advance, fall detection algorithms change. We reviewed fall detection algorithms adopted in most cited work since 2014, and found the tendency by comparing with the algorithms most cited work before 2014. Figure 2 shows the comparison of changes of between the period after 2014 and before 2014. We compared this tendency from a coarse view to a fine-grained perspective. The main categories of fall detection algorithm are presented firstly. It should be noted that the conventional algorithms that we defined include threshold-based, rule-based and shape-based algorithm. With regard to fall detection algorithms used before 2014, we used the data on most cited paper on fall detection in [PERSON] and coworkers' previous survey paper [2]. As mentioned previously, the accelerometer was a mainstream sensor used by fall detection system in early days. Since it can only get velocity and acceleration of one part of human body wearing devices, the thresholds-based method became the preferred method. For camera-based methods, the fall detection algorithms always adopted machine learning. In Figure 2a, these two kinds of algorithms were almost equally adopted in fall detection systems. The fall detection method based on machine learning is not a general solution, thus diverse methods were adopted, as shown in Figure 2c. For fall detection algorithms used after 2014, the fall detection algorithms based on machine learning become a new mainstream, as shown in Figure 2b. Since the increased new types of advanced sensors can perceive more detail of human activities, the threshold-based algorithm is inadequate to achieve the goal. From the aspect of the specific type of algorithm, Support Vector Machine (SVM) and Decision Tree (DT) are still the first two most used algorithms, same as the situation before 2014. Based on Kinect, [PERSON] et al. [10] used DT to compute a confidence that a fall preceded on a ground event. Using an accelerometer built-in smartphone, [PERSON] et al. [18] chose DT for retrieving features and threshold information to detect a fall as well. Using accelerometer, [PERSON] et al. [22] employed SVM to select acceleration thresholds. These algorithms not only trade off performance for sensitivity and specificity, but also are appropriate and effective for small datasets. Furthermore, with the introduction of RF sensors, such as Wi-Fi and Doppler radar, digital signal processing methods have been employed in fall detection systems, such as Wavelet transform (WT). According to a ceiling mounted Doppler range control radar, [PERSON] et al. [17] used WT to detect human falls. [PERSON] et al. [25] used the signal processing algorithms and techniques involved in elderly fall detection using radar as well. In addition, [PERSON] et al. [30] presented a real-time, contactless, low-cost accurate indoor fall detection system, named as RT-fall, using the phase and amplitude of the fine-grained Channel State Information (CSI) in 2017. Just after a few months, this work has been quoted for more than 20 times. Although is not one of the top twenty most cited papers after 2014,  
Figure 2: Tendency of algorithms used in fall detection system: (**a**) main categories of algorithm used before 2014; (**b**) main categories of algorithm used before 2014; (**c**) detailed categories of algorithms used before 2014; and (**d**) detailed categories of algorithms used after 2014. Noted that some fall detection systems use two algorithms. due to being published recently, it has a significant impact on others. These works based on RF sensor indicate a new way differing from the past, showing a kind of future direction of health care in-home. Finally, there is an interesting phenomenon, that is, deep learning as one of the popular machine learning algorithms, has not been adopted in the twenty most cited papers.