## 2 Mini Survey
### State-of-the-Art HAR Studies  
Although several survey papers [43; 45; 46; 52; 60; 64] have cited more than 200 related research papers, these represent just a fraction of the numerous HAR studies that have been conducted to date. The numerous papers show the limited terms of years and fields of applications, including overlapping scopes among the survey papers. Moreover, several survey papers have reported earlier reports that present a comprehensive literature review. For this research field, numerous research and survey papers have been published over a short period. That trend is expected to continue. For our mini-survey, Table 2 presents representative HAR studies reported between January 2019 to June 2021. We specifically emphasized a review of recognition methods in the third column and benchmark datasets in the fourth column. \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
2019 & [PERSON] et al. [51] & & ✓ & ✓ & & & & 187 \\
2019 & [PERSON] et al. [52] & ✓ & & & & ✓ & ✓ & 208 \\
2019 & [PERSON] et al. [53] & & ✓ & & ✓ & & 48 \\
2019 & [PERSON] et al. [54] & & & ✓ & & & 141 \\
2019 & [PERSON] et al. [55] & & & & & ✓ & & 9 \\
2019 & [PERSON] et al. [56] & & & & ✓ & & ✓ & 106 \\
2019 & [PERSON] et al. [57] & & ✓ & ✓ & & & ✓ & 149 \\
2019 & [PERSON] et al. [58] & & ✓ & ✓ & & ✓ & ✓ & 119 \\
2019 & [PERSON] et al. [59] & & & ✓ & ✓ & & ✓ & 77 \\ \hline
2018 & [PERSON] et al. [60] & & & ✓ & & & ✓ & 275 \\
2018 & [PERSON] et al. [61] & & ✓ & ✓ & & ✓ & ✓ & 83 \\
2018 & [PERSON] et al. [62] & & & ✓ & & ✓ & ✓ & 63 \\
2018 & [PERSON] et al. [63] & ✓ & & & & ✓ & 182 \\ \hline
2017 & [PERSON] et al. [64] & & ✓ & ✓ & ✓ & & 225 \\
2017 & [PERSON] et al. [65] & ✓ & & ✓ & & & 78 \\
2017 & [PERSON] et al. [66] & & ✓ & ✓ & & & 65 \\
2017 & [PERSON] et al. [67] & & ✓ & ✓ & & & 88 \\
2017 & [PERSON] et al. [68] & & ✓ & & & & 39 \\ \hline
2016 & [PERSON] et al. [69] & ✓ & & & & ✓ & 66 \\
2016 & [PERSON] et al. [70] & ✓ & & & & & 90 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Authors** & **Methods** & **Datasets** \\ \hline
2021 & [PERSON] et al. [71] & IPL-JPDA & original \\
2021 & [PERSON] et al. [72] & CML (RF) & original \\
2021 & [PERSON] et al. [73] & YOLO+CNN & original \\
2021 & [PERSON] et al. [74] & CML (RF) & [123] \\
2021 & [PERSON] et al. [75] & 4-CNN-LSTM & [124] \\
2021 & [PERSON] et al. [76] & CNN-LSTM & [124; 125] \\
2021 & [PERSON] et al. [77] & ConvLSTM & original \\
2021 & [PERSON] et al. [78] & CNN-BiLSTM & [124; 126] \\
2021 & [PERSON] et al. [79] & DIM-BLS & [124; 126] \\ \hline
2020 & [PERSON] et al. [80] & CML (SVM) & [127] \\
2020 & [PERSON] et al. [81] & BLSTM & original \\
2020 & [PERSON] et al. [82] & CML and CNN & original \\
2020 & [PERSON] et al. [83] & CML & [128] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Representative HAR studies reported between January 2019 and June 2021. The most commonly used method is CNN-LSTM (e.g., 9 out of 51 studies): a model based on CNN with various backbones combined partially with LSTM [23] layers, including ConvLSTM [153]. Bidirectional LSTM (BLSTM) [154] was used solely or combined with CNN as CNN-BLSTM. Furthermore, CML-based and DL-based ensemble learning (EL), which comprise bagging, boosting, and stacking, are used commonly as HAR methods. For CML-based methods, support vector machines (SVM) [155], random forests (RF) [156], and \(k\) nearest neighbors (\(k\)NN) were used. Various CNN backbones have been used: LeNet [157], Inception [158], VGG-Net [159], Xception [160], ResNet [161], and ResNet's derivative models in parentheses if they are indicated explicitly in the references.