## 6 Evaluation  
### Experimental Settings  
In order to evaluate the effect more accurately, this paper adopts F1 value as the evaluation index of the model effect. F1 value is an improved evaluation index based on Precision and Recall. The specific meanings of these indicators are introduced below:  
* Precision. The precision rate refers to the proportion between the number of truly true samples and the number of samples judged to be true by the model. From the perspective of prediction results, precision rate describes the specific proportion of samples that are positively predicted by the model.
* Recall. Recall rate refers to the proportion of all samples of a certain category that can be correctly found by the model. Recall rate describes the ability of the model to find such samples from all samples from the perspective of a certain category of samples. The higher the recall rate is, the more accurate the model can find such samples from all samples.
* F1 value comprehensively considers the precision rate and the recall rate and can describe the performance comprehensively. Compared with using the precision rate or recall rate alone, using F1 value as the evaluation index can more accurately and comprehensively describe the effect and prevent the model from cheating.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Range** & **Mean (SD)** \\ \hline Age (years) & 19–25 & 23.1 (1.7) \\ Height (cm) & 159–182.7 & 174.3 (13.9) \\ Weight (kg) & 49–76 & 63 (19.7) \\ Numbers of labeled data & 50–982 & 721 (89) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of the 7 participants in the dataset.  
Figure 10: Proportion and number of different labels. Furthermore, the number of each sensor readings visualized.  
This paper will use the evaluation measurements mentioned above to monitor the training process of the model, and better display the changes of the model in the training process by drawing these evaluation indicators into images.  
PyTorch framework is used to build and train the neural network model. The development and training environment of Marfusion activity recognition model is shown in the following Table 3. As for the Convolution Neural Network (CNN) structures, we applied 4-layer CNN and one-layer pooling to extract features of different modalities.  
For fair comparison, we apply the manner of splitting dataset following the settings in the state-of-the-art approaches [10, 11, 23, 33]. In addition, these state-of-the-art studies [10, 11, 23, 33] evaluated their models on publicly available datasets, which are similar as our dataset with multiple individuals. Thus, we apply the same setting as these state-of-the-art studies to validate our model in a fair experimental setting. Before starting the model training, all samples in the dataset were divided into two parts, 80% of which were used for model training and 20% for testing the training effect and generalization ability. During the training, the batch size was set to 32, and the total loss in each epoch was recorded. For each epoch training, an accuracy calculation was performed. When the loss value becomes stable, the training is stopped and the model is saved on the hard disk.  
The prediction value and true value will be used to calculate the current F1 value for each epoch training. The training was stopped when the epoch loss no longer decreased and the model tended to be stable.