# A Depression Recognition Method Based on the Alteration of Video Temporal Angle Features
## 4 Experimental Studies
### Experimental
#### 4.4.2 Experimental Results Comparison  
The methods employed in this paper involve extracting one-dimensional or multi-dimensional modal features from the patient's face, voice, and text data. These extracted features are then used for training and verification on various neural networks. Table 4 provides a comprehensive comparison between our developed unimodal depression algorithm based on facial features and previous studies utilizing different modalities. The designed features demonstrate commendable performance in terms of precision rate, recall rate, and F1 score, achieving values of 0.77, 0.83, and 0.80, respectively. [PERSON] et al. [41] obtained the result of a mixed model of multiple neural networks, including CNN and LSTM, with a recall rate of 0.91. However, the precision rate of this algorithm is only 0.64, resulting in a final F1 of 0.75, which is 0.05 lower than the one proposed algorithm in this paper. Additionally, the F1 scores of [PERSON] [14] and [PERSON] et al. [42] in the audio single-mode design algorithm are 0.30 and 0.34 lower, respectively, compared to the performance achieved by the features designed in this paper. Text is considered unimodal due to its characteristic of containing precise information. The F1 score of the text single-modal feature proposed by [PERSON] et al. [13] is 0.23 lower compared to this paper. [PERSON] et al. [39] and other researchers effectively utilize text features by employing the BGRU neural network for processing text information after performing word embedding (Word2 Vec) on the text data, resulting in an F1 value that is 0.04 higher compared to ours. Additionally, when comparing with the multimodal detection algorithms proposed by [PERSON] [44] and [PERSON] et al. [45], it is evident that the algorithm presented in this paper outperforms them in terms of precision, recall, and F1 scores when utilizing mixed modes. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Methods** & **M** & **P** & **R** & **F1** \\ \hline [PERSON] et al., 2021 [41] & A & 0.64 & 0.91 & 0.75 \\ [PERSON] et al., 2022 [14] & A & 0.51 & 0.50 & 0.50 \\ [PERSON] et al., 2022 [42] & A & 0.69 & 0.35 & 0.46 \\ [PERSON] et al., 2019 [13] & T & 0.67 & 0.50 & 0.57 \\ [PERSON] et al., 2020 [43] & T & 0.85 & 0.83 & 0.84 \\ [PERSON] et al., 2022 [44] & A + T & 0.53 & 0.57 & 0.53 \\ [PERSON] et al., 2019 [45] & A + T & 0.60 & 0.75 & 0.67 \\ [PERSON] & V & 0.77 & 0.83 & 0.80 \\ \hline \hline \end{tabular} Note: M = Modality in the table refers to the modality type. V = Visual refers to the visual modality data. A = Audio refers to the audio modality data. T = Text refers to the text modality data. + refers to the corresponding mixed multimodal.  
\end{table} 
Table 4: Comparison of the impact of video modality versus non-video modality data. To objectively evaluate the algorithm proposed in this paper, we compare the video modality used in this paper with other algorithms, including video modality. The comparison results are presented in Table 5.  
The results presented in Table 5 demonstrate that the features proposed in this study yield remarkable improvements in algorithm performance. [PERSON] et al. [47] achieved a precision rate of 0.89 in their three-modal (visual + audio + text) fusion, surpassing the pure visual mode in this study by 0.12. However, the recall rate was adversely affected, leading to an F1 value 0.1 lower compared to this paper. [PERSON] et al. [49], utilizing the Causal Convolutional Network (C-CNN), achieved a similar recall rate to this study in multimodal analysis. However, their F1 value was 0.03 lower compared to this paper. Conversely, both [PERSON] [28] and [PERSON] [46] utilized visual modality data in their studies. In general, the algorithm proposed in this study excels at extracting features from the pure visual modality, leading to superior outcomes. In the DAIC-WOZ dataset [12], we are provided with several types in terms of visual modal data, including head Pose, eye annotation direction Gaze, and 2D face68 key points.