# Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm
## 2 Related Works  
Computational methods for gait phase recognition fall into two main categories. The first category is comprised of algorithms, which divide the gait phases based on the threshold selection of either raw or processed data [21]. Secondly, some deep-learning approaches have emerged in recent years to substitute the aforementioned techniques that relay traditional classification algorithms. Some have applied deep learning algorithms to different types of sensors to detect gait phases. For instance, [PERSON] et al. [22] present a fully automated frontal (i.e., employing front and back views only) gait phase recognition approach using the depth information captured by multiple Kinect RGB-D cameras. However, the captured image information is easily disturbed by the external environment. [PERSON] et al. [14] proposed a method of hierarchical clustering to achieve recognition of human gait phase by processing electromyography (EMG) data collected during gait, which has improved the above-mentioned problems that are susceptible to environmental interference. However, muscle electrical signals are susceptible to factors such as sweat when collecting EMG data. [PERSON] et al. [23] further improved the problems of the above EMG method, and proposed a proportional fuzzy (PBF) algorithm to achieve smooth recognition of gait phase for foot pressure information processing, but the foot pressure will be affected by the wearer's weight, load, and other factors [19]. For participants with large differences in weight, we need to constantly recalibrate the threshold of the pressure sensors. Besides, the pressure sensor constantly rubs in the middle of the insole, thereby shortening its life [24]. Therefore, the use of whole inertial measurement units (IMU) has risen lately. This is mainly due to the fact that more information can be obtained by adopting a small number of inertial sensor modules, and most of the inertial sensor modules are installed on the legs and feet, so as to avoid damage or discomfort to the wearer [25]. At the same time, the information of IMU is basically unaffected by human body weight, sweat, and other factors, which is a prominent advantage compared to the method of plantar pressure or muscle electrical signal detection. In addition, inertial sensors are extremely cost-effective [26], and the acceleration signals acquired by inertial sensors exhibit typical waveform characteristics during the gait cycle [27]. More recently, the use of this type of sensor to collect movement data has increased rapidly [28]. Previous studies have also used acceleration to detect gait time practice [29], but there are few studies using acceleration sensors to detect gait phase. This paper builds a system that uses three inertial sensor modules to obtain the acceleration information of the lower limbs of the human body. The collected acceleration data was reduced by the principal components analysis (PCA) algorithm, which focuses on extracting the feature information of the original data and searches for a set of orthogonal low-wiki functions to represent a set of high-dimensional data, improving the recognition rate and recognition speed [30]. Then the paper divides the human gait into two phases and proposes a method of dividing the two gait phases. Finally, this paper proposes a LSTM-DNN algorithm for detecting gait phase. The designed LSTM-DNN with higher accuracy will be further evaluated with learned and unlearned data to test its suitability with acceleration classification. For the recognition system, this paper designs an effective and adaptable gait detection method. Foreign research results [19] indicate that a large amount of information can be obtained by using a small number of acceleration sensors that are located on the legs and feet to minimize sensor damage and discomfort to the person wearing the sensor. In this study, a speed sensor data acquisition device is developed to obtain gait information for human lower limbs for gait detection.