# Car-Sense: Vehicle Occupant Legacy Hazard Detection Method Based on DFWS
## 4 Method Design
### Personnel Status Identification
The mean, variance, and standard deviation are calculated separately to form the feature vector, and the feature vector is normalized to form the dataset, of which 80% is used for training and 20% for offline testing. **Tooling Definition. ** In order to determine the best results of the system under which time slice, we set different time slice lengths and experimented with three different models, KNN, SVM, and MLP, respectively, and concluded after extensive experimental analysis that the best results were achieved when using the KNN algorithm. Moreover, the classifier has a relatively good performance when t = 20 s. This is due to the fact that it measures the absolute distance between points in the multidimensional space. Reflected in this experiment is the measurement of the mean and variance of the CSI time domain signal caused by the difference between multiple dimensional data. We define and train the model using the Keras framework. The collected data are also trained, and the weights and biases of the parameters are adjusted according to the degree of deviation of the output values from the expected values while a portion of the data is selected for validation. After having a more accurate model, we try to deploy the model to the cloud platform, which will save the model file in a special format to ensure the speed of identification, so that it can perform timely and accurate data analysis for identification. Figure 8: RSSI waveforms for different personnel positions in the vehicle. ### Impact of User Diversity

Due to the existence of different reminders for different persons, adults and children have especially huge differences in body size and have different effects on wireless signals. In order to verify the recognition effect of the model on people with different body sizes, we set up a child group and an adult group for data comparison and compared the recognition accuracy of different people under the premise of ensuring the consistency of other indicators. In addition, we also explored the discrimination accuracy of general objects (using larger plush toys) instead of real people in a vehicle environment. The correct test rates are shown in Figure 10, respectively. Several results can be drawn from the figure: (1) There is a 10% improvement in recognition of adults compared to children, probably due to the higher amplitude of movement, which has a significant effect on the signal resulting in higher accuracy. (2) The 100% recognition result at 0 can be attributed to the absence of human activity in the LOS area, where the signal fluctuation is essentially zero. (3) Children are smaller than adults, so there is a greater discount in recognition, but despite this, the system is still able to maintain an accuracy rate of over 65%. (4) The recognition of individual person locations is consistently above 80%, an advantage that is very relevant in the case of dangerous situations where individual children are stranded in the vehicle. (5) When a larger plush toy was introduced instead of a person for comparison, it was found that although the doll was larger, it had almost no effect on the recognition effect, and the difference in recognition effect between the presence or absence of the toy was not significant, this is because the toy does not move in the LOS area, which is then equivalent to \"invisibility\" for the CSI signal\". Although the performance of Car-Sense is not very bright in terms of the multi-person recognition effect, but oriented to perceive the general situation where a single

Figure 10: Accuracy of recognition of different categories: (**a**) different number of adults; (**b**) different number of children; (**c**) position when a single person; (**d**) comparison of persons and dolls. Figure 9: Vehicle interior equipment deployment. person is enclosed in a car in a dangerous situation, Car-Sense can achieve satisfactory experimental results.