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, Shao 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 Contreras-Vidal 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, Huang 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, Harvey Grove et al. targeted the shank muscle nerves of a male thigh amputee and moved them to the thigh [
13]. The average recognition rate was about 94% when the knee joint was flexed and extended, the ankle joint was dorsiflexed, and the tibia and femur were rotated. In the field of sensor signal research, Tong et al. used gyroscope signals to obtain joint angles and proposed the dynamic positioning system [
14]. Young et al. collected sensing information of the lower limb exoskeleton itself, such as acceleration, angular velocity and pressure data. They manually selected features and used linear discriminant analysis classifier to solve the recognition problem of five movement modes and transformations of five modes [
15]. The recognition rate reached 93.9%. Compared with bio-sensory sensors and physical interaction sensors alone, the method of multi-source information fusion can effectively increase the identification accuracy and reduce the prediction delay. Therefore, Young et al. proposed to use the dynamic Bayesian network method to integrate the sEMG signals and Inertial Measurement Unit (IMU) signals in the characteristic layer to identify the basic locomotion modes on the basis of the existing research. Subsequent studies that included a time history of exercise data reduced the steady-state pattern error recognition rate by 50% under deep belief network (DBN). It has a certain hysteresis, because the collected data is between the stance state before the locomotion transition and the swing state after the transition [
16]. In addition, Chen et al. innovatively proposed and verified that 6 movement patterns can be recognized by collecting capacitive signals on the affected limbs of patients [
17], the average identification accuracy can reach 93.4%. The image-based recognition method has scene limitation. EEG and sEMG sensors are not easy to wear and are easily affected by sweat, body temperature and other factors. Although the sensor information of the exoskeleton robot can intuitively judge the human locomotion pattern, the recognition result is not satisfactory.
In order to solve the above effects, this paper proposes a locomotion pattern recognition method of exoskeleton robot based on SA-SVM. SA is one of the earliest and most popular meta-heuristic algorithms, which can effectively solve the parameter optimization problem of the SVM model. It introduces the natural mechanism of solid annealing as well as the appropriate random elements. The value of randomness in the whole solution neighborhood can avoid the algorithm falling into local optimum and improve the ability of global optimization.
In order to identify the locomotion pattern stably and accurately, the SA-SVM model is adopted in this paper based on traditional SVM. Firstly, lower limb IMUs information, joint angle information and plantar pressure information were collected. Then, IMUs information, joint angle information and force sensing resistors (FSRs) data were combined as the input of SA-SVM model to further improve the overall locomotion pattern recognition model. The model effectiveness and accuracy of the multi-mode machine learning model are also verified.