## 2 Mini Survey
### Existing HAR Surveys  
For more than a quarter-century, HAR has been studied using various and numerous approaches [24]. Through that period, HAR studies have been boosted mainly by the rapid progress of computer and sensor technologies and their dissemination to our society. Numerous HAR studies have been conducted. Currently, HAR presents a challenging task because it extends to various technological domains that include, but which are not limited to, sensor selection, sensor calibration, recognition algorithm development,parameter optimization, real-time processing, improved accuracy, privacy consideration, and robustness for individual differences. Ultimately, HAR students examine human understanding from a behavioral perspective [25].  
Numerous technical and academic papers describing HAR studies have been published each year. In addition, numerous survey papers have been presented since the advent of the first HAR studies [26; 27; 28]. Table 1 presents a summary of HAR survey papers published between January 2016 and October 2021.  
We classified sensing modalities into four categories: cameras, smartphones (SM), wearable sensors (WS), and environmental sensors (ES). Although cameras are included in ES, we specifically examine them as an independent category for the following three reasons. The first reason is that they are related to an actively researched pattern recognition problem in the computer vision field [29]. The second reason is that large amounts of high-dimensional pixel data can be obtained from cameras. In addition to still images, video sequences were used as spatiotemporal features for improved recognition accuracy [30]. Moreover, red, green, and blue-depth (RGB-D) cameras were widely used [31] to obtain depth data in each pixel. The third reason is the need for preservation and awareness of privacy. As a simple and convenient approach in the pursuit of privacy, image resolutions are reduced with degradation of spatial and temporal image quantity and quality. However, this measure will also degrade the resultant recognition accuracy [32].  
We roughly divide recognition methods into two types: conventional machine learning (CML)-based methods and DL-based [21] methods. With respect to recognition accuracy, DL has appreciable performance and superiority over CML in numerous and varied applications [33]. The comparative application potential for real-time processing under a limited computing resource was the most important benefit of using CML. By virtue of the progress of edge computing technologies [34], cost-effective and advanced performance systems are available without using cloud computing, expensive graphics processing units (GPU), or a special analog-computing device [35].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Pub.** & **Authors** & \multicolumn{3}{c}{**Sensing Modality**} & \multicolumn{3}{c}{**Approach**} & **Num.** \\
**Year** & **(A–Z)** & **Camera** & **SM** & **WS** & **ES** & **CML** & **DL** & **Ref.** \\ \hline
2021 & [PERSON] et al. [36] & ✓ & & ✓ & & ✓ & 48 \\
2021 & [PERSON] et al. [37] & ✓ & & & ✓ & & ✓ & 113 \\
2021 & [PERSON] et al. [38] & & ✓ & ✓ & ✓ & & ✓ & 191 \\
2021 & [PERSON] et al. [39] & & & & ✓ & & ✓ & 64 \\
2021 & [PERSON] et al. [40] & ✓ & ✓ & & & ✓ & ✓ & 115 \\
2021 & [PERSON] et al. [41] & ✓ & & & & ✓ & 150 \\
2021 & [PERSON] et al. [42] & & ✓ & ✓ & & ✓ & ✓ & 140 \\ \hline
2020 & [PERSON] et al. [43] & ✓ & & & & ✓ & ✓ & 237 \\
2020 & [PERSON] et al. [44] & & ✓ & ✓ & ✓ & & & 136 \\
2020 & [PERSON] et al. [45] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & 236 \\
2020 & [PERSON] et al. [46] & & ✓ & ✓ & & ✓ & ✓ & 219 \\
2020 & [PERSON] et al. [47] & ✓ & & ✓ & & & & 183 \\
2020 & [PERSON] et al. [48] & ✓ & & ✓ & ✓ & & & 165 \\
2020 & [PERSON] [49] & ✓ & & & & & ✓ & 60 \\
2020 & [PERSON] [50] & ✓ & & ✓ & ✓ & ✓ & ✓ & 132 \\ \hline \hline \end{tabular}
\end{table}
Table 1: HAR survey papers between January 2016 and October 2021.