# Fall Recognition Based on Time-Level Decision Fusion Classification
## 2 Related Work
### Multimodal-Based Methods  
Other studies have utilized data from various sensors such as cameras, accelerometers, radars, vital signs, gyroscopes, and magnetometers to detect falls or recognize actions [23; 24; 25; 26; 27]. In these studies, falls were detected in a multimodal context by extracting features from signals acquired from two or more sensors and then aggregating these features to be used as input for the classifiers. Multimodal-based approaches can achieve high accuracy by integrating data from various sensors. However, processing time may be compromised as values from different sensors need to be aligned and handled, and the system design can become more complex. The advantages and disadvantages of detecting falls using various sensors, as well as the strengths and weaknesses of various vision-based fall detection algorithms under investigation in this paper, are summarized in Tables 1 and 2, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor** & **Pros** & **Cons** \\ \hline Accelerometer & Sensor sensitivity & Limitation in catching the global movements of the body caused by the number of attached sensors. Devices have to be worn. \\ \hline Radar & Low-light condition performance & Limitation in recognizing different postures. \\ Privacy friendliness & Difficulty in distinguishing between the fall of objects and humans. \\ \hline Vision & Capture of global body posture is possible & Vulnerability in low-light conditions. \\ No requirements are imposed on users & & \\ \hline Multimodal & Enhancement in performance through the utilization of abundant information & Decreased inference time due to processing of various sensor values. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of pros and cons of fall detection methods using various sensors.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Pros** & **Cons** & **Dataset** \\ \hline [PERSON], 2018 [15] & Fit a person’s silhouette on the ellipses and observe the changes in the ellipses and detect fall. In general, the extraction accuracy of silhouette is higher than skeleton coordinate prediction accuracy, so the performance degradation due to the silhouette step is less. & It is difficult to detect detailed body movements because the method creates an ellipse by fitting silhouettes and observes changes in the ellipse to detect falls. & UR-Fall silhouettes and observes changes in the ellipse to detect falls. \\ \hline [PERSON], 2018 [17] & It is possible to extract the temporal characteristics of the skeleton sequence because the predicted skeleton is used as an input for the LSTM & There is less temporal information using only 8 frames of input. And it is difficult to convey the relationship between the joints because the skeleton coordinate value itself is used as the input. & Private dataset \\ \hline [PERSON], 2021 [16] & This study uses 3D skeleton sequences as inputs and classifies them using ST-GCN models, so all spatial–temporal information can be considered. & Since 3D skeleton predictions are less accurate than 2D skeleton predictions, incorrect 3D coordinate predictions are likely to occur in a real-world environment. This may result in performance degradation. & HST v2 \\ \hline [PERSON], 2021 [18] & The authors present a comparison of various ML models as classifiers. & Because it detects falling in frame, input data do not contain temporal information. & HAR-UP \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vision-based fall detection studies with some pros and cons.