## 1 Introduction  
Applying intelligent rehabilitation techniques, such as rehabilitation robots [1; 2; 3; 4; 5; 6; 7] or in-home rehabilitation systems based on wearable sensors [8; 9; 10; 11; 12], to help people suffering from limited mobile ability to perform physical rehabilitation is of significance. Such intelligent techniques alleviate the shortage of physical therapists and realize data monitoring for more accurate evaluations on patients [13; 14; 15; 16], and thus have become a new trend in recent years. The traditional scenario for lower limb rehabilitation is level walking. In recent years, terrain classification techniques enable wearable assistant systems to recognize more terrains, which allows patients to perform rehabilitation training in more complex scenarios, and therefore has aroused wide attention [17; 18; 19]. Among all terrains, uphill, level ground, and downhill are the three most common terrains in daily life, and thus are widely investigated in the study of terrain identification [19; 20; 21; 22]. The input signals of these kinds of rehabilitation-driven systems for terrain classification include kinematics information (e.g., inertial measurement units (IMUs)), kinetics information (e.g., ground reaction force and interaction force), and electromyography (EMG) signals [23; 24; 25; 26; 27]. Using the input signals mentioned above, increasing efforts have been made for classifying different terrains. For example, in [28], five time-domain features of the EMG signals from eight muscles on the thigh were extracted and analyzed. Ramp and level walking were classified for thirteen healthy participants and the average classification accuracy for all terrains was 92.9%. In [27], signals from 13 IMUs on the robotic legs of six transfemoral amputee subjects were collected and used to classify different terrains and an average accuracy of 81.2% was reached in recognizing ramp and level ground. In [22], the authors proposed a terrain identification method, namely muscle synergies, based on the EMG signals from twelve muscles. The data from seven able-bodied participants were collected and were classified as level ground and ramp with an average accuracy of 83.8%. However, in general, using a single sensor type lacks in a comprehensive detection of the bodily changes of the user, and thus is not adequate for high-accuracy terrain recognition [17; 19]. To obtain multi-dimensional information, the fusion of multi-sensor data, together with machine learning-based sensor fusion algorithms, is widely used in terrain identification in recent years. Some studies report the integrated use of accelerometers, angle sensors, kinematic data captured by camera, EMG signals, etc., to differentiate terrains [20; 21; 29; 30; 31]. For example, in [32], nine EMG and six ground reaction force (GRF) sensors were utilized, based on which five terrains were identified continuously in four gait phases using the linear discriminant analysis (LDA) and support vector machine (SVM) methods, and the ramp-level ground classification accuracy in this work was averaged at 97.73%. In [20], four healthy subjects performed walking activities on uphill, level ground and downhill, and a high average terrain classification accuracy of 93.4%, based on the Bayesian LDA, was achieved through developing a technique consisting of an interaction force sensor, four EMG sensors, four GRF sensors, and a position sensor. In [21], the data from seven healthy subjects collected by seven EMG sensors and two accelerometers were obtained and used to classify the terrain transition from level ground to stairs or ramp; from this, the classification performance of the EMG and accelerometer signal was compared and discussed, proving that the fusion of both signals may contribute to a more robust classification.