# SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot
## 1 Introduction  
Locomotion pattern recognition plays an important role in the control of exoskeleton robots. In some practical situations, the exoskeleton robot often needs to understand the current locomotion pattern before it can make corresponding movements to complete the characteristic tasks. Accurate recognition of locomotion patterns is the premise of compliance control of exoskeleton robots. Accurate locomotion pattern recognition is the basis of exoskeleton control. The accuracy of locomotion pattern recognition results is closely related to excellent human movement data. At the present time, there are many kinds of locomotion pattern recognition methods. The ways can be divided into image-based methods [1], biology-based methods [2; 3] and sensor-based methods [4] according to the approaches of obtaining information. The first method is based on video image, which obtains the movement image sequence of human body through camera and analyzes the locomotion pattern after image processing. It is not suitable for wearable exoskeleton because this method has limitations on application scenes. The second is based on human physiological electrical signals. Human bioelectrical signals are electric potential signals that contain human behavioral intentions, which are transmitted to relevant tissues or organs by stimulation. The human body's intentions can be sensed by identifying the triggered electrical signals. Electroencephalography (EEG) [5] and surface electromyography (seems) [6] come from the bioelectrical signal related to muscle contraction, which will not cause physical damage to the exoskeleton wearer. The signal acquisition technology is mature and easy to operate. However, the surface electrodes that collect electrical signals are susceptible to sweat on the skin surface, which will cause serious interference with the data and lead to errors in human locomotion pattern recognition. The third method based on locomotion sensors is mainly to acquire human locomotion intention by collecting movement information or force information of the users, such as angle, speed, acceleration, etc. These sensor data are not susceptible to external interference and have good stability, but there are also some problems such as signal delay, drift and error accumulation. Usually, the human locomotion pattern recognition algorithm uses a single type of feature or a simple series of multiple features to achieve the fusion of multiple features. However, [PERSON] et al. used a new spectrum coding algorithm called Kernelized Multiview Projection (KMP) [7] to better integrate heterogeneous feature representations used in human locomotion pattern recognition. They respectively use Histograms of Oriented Optical Flow (HOF) [8], Histogram of Gradient (HOG) [9], Local Binary Pattern (LBP) [10] and color histogram to represent features. KMP has demonstrated a leading position in human locomotion pattern recognition across five popular human locomotion data sets [7]. In terms of EEG research, at the university of Houston, a team led by [PERSON] reduced the dimension of the characteristic matrix and combined it with local Fisher discriminant analysis data by extracting the characteristics of amplitude-modulated EEG signals. Additionally, a gaussian mixture model is used to change the exoskeleton robot state information and the mapping of the characteristic matrix, which finished the sit, stand, turn left, turn right research under the specific actions of brain electrical decoding technology [11]. The classification recognition rate can reach 97.7%. In terms of EMG research, [PERSON] et al. sent 11 channels of EMG signals collected from the stump and hip of lower limb amputees into a linear discriminant classifier to dynamically identify seven movement patterns offline [12] with a recognition accuracy of 91.6%. To further investigate the effect of EMG signal activity, [PERSON] et al. targeted the shank muscle nerves of a male thigh amputee and moved them to the thigh [13].