# Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm
## 5 Discussion  
This study demonstrated the capability of the proposed system to detect gait phases based on acceleration signals. To support this hypothesis, this paper proposes to use the LSTM-DNN to identify the gait phase, and compares it with other algorithms to verify the effectiveness of the algorithm. Walking activity emanated from the human body is important, and this information can be extracted through the use of acceleration signals. Although the LSTM-DNN algorithm has shown certain validity in the classification of acceleration signals for gait event detection, it still needs to be further optimized in the future. In this study, the inertial sensor module needed to be placed at a designated location on the instep, lower leg, and thigh of each subject. However, due to the height, weight, gender, etc., of each tester, the sensor cannot be accurately placed in the specified position and can only be installed at an approximate designated position, which needs further investigation. To characterize acceleration signals, there are three main cascaded modules which are data-processing, feature extraction, and classification methods. Feature extraction is the crucial step. The research result in this study shows that the LSTM-DNN algorithm can accurately identify the phase through the five features selected in this paper. By observing Tables 2 and 3, we can get that the kernel function has certain influence on the performance of SVM and the influence of the choice of K value on KNN is also very important. By analyzing the results of Tables 4-6, it can be shown that the SVM and KNN have low accuracy of the gait phase detection, but the proposed LSTM-DNN algorithm in this paper can significantly improve this situation. In reviewing the literature, the reliability of the accelerometer was questioned. It is interesting to note that the speed sensor had to be worn in the designated position of each subject's foot, calf, and thigh. However, due to the difference in the height, weight, and gender of the test subjects, there may be slight differences in the position of the sensor. According to Figures 8 and 9, it can be found that the recognition accuracy and F-score from the LSTM-DNN algorithm have been as high as 91%, which indicates that the generalization performance of the LSTM-DNN algorithm is better, and it is less affected by factors such as gender, height, weight, and pace. The purpose of this study was to apply machine learning to predict stance and swing phases from acceleration signals. In support of the hypothesis aforementioned, this study proposes the LSTM-DNN algorithm and uses it to successfully predict stance and swing phases. The results of learned data indicated that the acceleration signals had low variability and stability during walking on the ground. The LSTM-DNN algorithm fuses the LSTM model with the DNN model and uses 36 LSTM networks. In the experiment, we conducted a lot of experiments and found that when the number of LSTM units is 36, the recognition effect is better. In addition, it is difficult to achieve the desired effect by simply relying on a single LSTM network structure. By introducing the DNN structure, the unity of the network structure can be improved, and the recognition effect can be improved. Thus, the model used in this paper can achieve higher recognition accuracy, and the F-score value is higher than 92%. It can be seen from Tables 4-6 that the F-score of the LSTM-DNN algorithm is the largest, followed by the LSTM algorithm, and the F-score of the KNN algorithm is the smallest. It can be seen from the performance of each classifier in F-score that the classification result obtained by the LSTM-DNN algorithm is more reliable. In terms of the generalization of the proposed system, this study revealed the LSTM-DNN algorithm achieved better performance in gait phase recognition. The LSTM-DNN algorithm detects stance and swing phases with higher recognition accuracy and F-score than other algorithms.