## 2 Mini Survey
### State-of-the-Art HAR Studies
For other methods, various models and algorithms have been used: improved pseudo-labels joint probability domain adaptation (IPL-JPDA), you look only once (YOLO) [162], multi-channel bidirectional LSTM (MCBLSTM), extreme learning machine (ELM) [163], zero-shot learning (ZSL) [164], and U-Net [165]. Open datasets for HAR studies and competitions are well maintained for benchmarking [166, 167, 123, 124, 125, 126]. Advantages of using open benchmark datasets are not only \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Authors** & **Methods** & **Datasets** \\ \hline
2020 & [PERSON] et al. [84] & Adaboost\(+\)CNN & [129, 130, 131] \\
2020 & [PERSON] et al. [85] & CNN-LSTM & [132, 133, 134] \\
2020 & [PERSON] et al. [86] & MCBLSTM & [135, 136] \\
2020 & [PERSON] et al. [87] & EL & [123] \\
2020 & [PERSON] et al. [188] & CML & [135, 137] \\
2020 & [PERSON] et al. [89] & CNN & [138] \\
2020 & [PERSON] et al. [90] & ZSL & [139] \\
2020 & [PERSON] et al. [91] & CNN (ResNet-101) & [140]+original \\
2020 & [PERSON] et al. [92] & CNN-LSTM & [124] \\
2020 & [PERSON] et al. [93] & CNN-LSTM & [141, 142, 143] \\
2020 & [PERSON] et al. [94] & CNN & [141, 142, 143] \\
2020 & [PERSON] et al. [95] & CNN (Fusion-ResNet) & [135, 144] \\
2020 & [PERSON] et al. [96] & BLSTM & original \\
2020 & [PERSON] et al. [97] & CML & original \\
2020 & [PERSON] et al. [98] & 3D-CNN-LSTM & original \\
2020 & [PERSON] et al. [99] & CNN & [124] \\
2020 & [PERSON] et al. [100] & H-LSTM & [124] \\
2020 & [PERSON] et al. [101] & LSTM-CNN & [145, 146, 147] \\
2020 & [PERSON] et al. [102] & CNN (Fusion-Mdk-ResNet) & [124, 126, 147] \\ \hline
2019 & [PERSON] et al. [103] & LSTM & original \\
2019 & [PERSON] et al. [104] & HMM & original \\
2019 & [PERSON] et al. [105] & CNN+TL & [125, 148] \\
2019 & [PERSON] et al. [106] & RNN (LSTM) & original \\
2019 & [PERSON] et al. [107] & RNN & [149] \\
2019 & [PERSON] et al. [108] & CML (\(k\)NN, SVM) & [142] \\
2019 & [PERSON] et al. [109] & GMM & original \\
2019 & [PERSON] et al. [110] & BLSTM & original \\
2019 & [PERSON] et al. [111] & HMM & original \\
2019 & [PERSON] et al. [112] & CML (SVM) & [150, 151] \\
2019 & [PERSON] et al. [113] & CNN & original \\
2019 & [PERSON] et al. [114] & EL & original \\
2019 & [PERSON] et al. [115] & CML (SVM+ELM) & original \\
2019 & [PERSON] et al. [116] & CML (SVM) & original \\
2019 & [PERSON] et al. [117] & EL & [124] \\
2019 & [PERSON] et al. [118] & EL & [147] \\
2019 & [PERSON] et al. [119] & CNN (Inception) & [124, 147, 152] \\
2019 & [PERSON] et al. [120] & CNN (LeNet) & original \\
2019 & [PERSON] et al. [121] & U-Net & [126, 145, 146]+original \\
2019 & [PERSON] et al. [122] & EL & original \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(Cont.\)that it saves time and costs for the development of sensor systems and data collection; it also facilitates performance comparison with other state-of-the-art methods. By contrast, because accuracy improvements have become saturated, the performance competition is becoming increasingly severe year by year. We regard original dataset developments for various scenarios, environments, and target persons as a fundamental and important research element for the wide varieties and scopes of HAR practical applications. Especially in recent years, the performance, duration, and size reduction in sensors are improving constantly. In addition to using commercially available sensors, the use of originally developed handmade sensors [12] can be expected to yield new applications according to applicable environments and restrictions. Although original benchmark datasets require a huge workload for data collection and their labeling for annotation, the development of an automatic data collection platform using an internet of things (IoT) [168] sensor devices provides a solution for workload reduction. Extension of studies and their applications can be anticipated in the furtherance of healthcare management [169], privacy consideration [170], and multimodal sensing [171] as advanced original system development [172].