# Sequence-Information Recognition Method Based on Integrated mDTW
## 4 Experiments
### Human Activity Recognition Experiments  
We present the experimental evaluation of our proposed method, mDTW+Transformer, on three human activity datasets: UCI-HAR, PAMAP2, and OPPORTUNITY. The datasets were chosen due to their popularity and representative nature in the field of human activity recognition. The experiments aim to demonstrate the effectiveness of our approach in comparison to the state-of-the-art (SOTA) methods. For the experimental evaluation, we followed a standard protocol for data preprocessing, which is borrowed from [30]. We compared our proposed method, mDTW+Transformer, with several SOTA methods on each dataset. The results of our experiments are summarized in Tables 1-3 for point matching and HCCR, respectively. In Table 3, the hyphens indicate that the given algorithm was not tested on the specific dataset. From the tables, it is evident that our proposed method, mDTW+Transformer, outperforms all the compared SOTA methods on all datasets in terms of accuracy. The results demonstrate the effectiveness of our approach in recognizing human activities from various datasets. The superior performance of our method can be attributed to several factors: (i) the combination of mDTW and Transformer allows for a robust alignment between different activity instances, (ii) the Transformer architecture captures long-range dependencies effectively, and (iii) the use of appropriate evaluation metrics ensures a fair comparison with SOTA methods. Furthermore, our method is generic and can be applied to various \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Top-1** & **Top-3** & **Top-10** \\ \hline Benchmark 2013 [18] & 95.3\% & 97.3\% & 98.8\% \\ VO-3 2013 [28] & 95.8\% & 97.8\% & 99.0\% \\ UWarwick 2013 [28] & 97.4\% & 98.4\% & 99.3\% \\ Runner-up 2013 [28] & 96.9\% & 98.2\% & 99.2\% \\ DropSample 2016 [26] & 97.2\% & 98.4\% & 99.4\% \\ Ensemble-9 2016 [26] & 97.5\% & 98.5\% & 99.5\% \\ NET4-subseq30 2017 [27] & 97.9\% & 98.9\% & 99.5\% \\ NET123456 2017 [27] & 98.1\% & 99.0\% & 99.5\% \\ RNN:Single 2019 [19] & 97.2\% & 98.4\% & 99.4\% \\ RNN:Ensemble 2019 [19] & 97.6\% & 98.6\% & 99.5\% \\ MSCS+ASA+SCL 2022 [29] & 96.7\% & 97.6\% & 98.9\% \\ SqueezeNext+CCBAM10 2023 [29] & 97.4\% & 98.6\% & 99.2\% \\ DLHCR 2024 [29] & 97.8\% & 98.7\% & 99.4\% \\ mDTW-Cluster & 68.7\% & 88.8\% & 94.6\% \\ mDTW+cGRU & 97.4\% & 99.1\% & 99.6\% \\ mDTW+Transformer & 98.5\% & 99.3\% & 99.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of HCCR methods. human activity recognition tasks without any modifications, making it a versatile approach for practical applications. Our experimental results with the mDTW+Transformer method outperform the state-of-the-art (SOTA) due to a key characteristic of our proposed approach: the coarse-to-fine classification method applied to sequences. The results are listed in Table 3. This method involves a two-step classification process that enhances both the accuracy and efficiency of classification. In the first step, coarse-grained classification, sequences are initially grouped into broad categories based on their overall features and patterns. This initial stage focuses on capturing the overall characteristics of the sequences, reducing the complexity of subsequent processing. In the second step, fine-grained classification, sequences within each broad category are further classified with greater precision. This stage emphasizes the subtle features and patterns within sequences, aiming to improve the accuracy of classification. By employing this coarse-to-fine classification strategy, our mDTW+CGRU method comprehensively considers the features of sequences and effectively captures complex patterns. Additionally, the CGRU architecture, with its powerful expression capabilities, effectively learns long-term dependencies within sequences, further enhancing the performance of classification. Therefore, we attribute the superior experimental results of our mDTW+CGRU method compared to SOTA to the coarse-to-fine classification approach.