# SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot
## 2 Materials and Methods  
### Data Collection and Processing  
The exoskeleton hardware system is based on ARM architecture. The core chip adopts 8-core A53 6818 processor, 2 GB RAM, 16 GB EMMC and supports HDMI. The main frequency is 2 GHz and the system uses RS485 high-speed 2 M transmission bus for data transmission, whose frequency is 100 Hz. After collecting the locomotion information of each part, the information is transmitted to the ARM main control computer through RS485 bus, so as to accomplish the drive and control of the exoskeleton robot.  
The lower limb locomotion data were collected by the exoskeleton sensing system. There are two main types of sensors used to collect data, inertial measurement units (IMUs) and force sensitive resistors (FSRs). The lower limb locomotion data collected mainly included thigh angle, shank angle and plantar pressure. The angle of the hip joint is the angle between the vertical and thigh orientation. The angle of the knee joint is the angle between the thigh and the shank. Calculated from thigh angle and shank angle. The joint angles of the exoskeleton robot were defined according to clinical gait analysis (CGA), as shown in Figure 1.  
Two pressure sensors FSRs (LOSON LSH-10) are placed on the heel and the ball of the foot respectively to detect the contact force between the sole and the ground, as shown in Figure 2. The measurement range of each FSR is 0-200 kg, the comprehensive accuracy is \(\pm\)0.5%F.S, the diameter is 20 mm, the thickness is 12 mm. It is easy to install and carry as the size is small and the weight is light. Because the FSRs output electrical signal is relatively weak, it needs to be equipped with an amplifier circuit. The output FSRs signal can be amplified to 0-5 V through this circuit, which is corresponding to the measured 0-200 kg mass and showing good linear characteristics.  
In order to improve the diversity of the experimental data and the generalization of the model, we selected six healthy men without gait defects as the study subjects. The subjects are 160 cm\(\sim\)180 cm in height and 50 kg\(-\)70 kg in weight. Before collecting the data of the subjects, the exoskeleton should be adjusted according to the height and weight of the subjects to ensure that each subject is wearing an independent exoskeleton and has adapted to the normal walking posture in a period of time. In addition, ensure that the sensor is in standard condition and calibrated. Exercise data were collected when the subjects were able to walk in a comfortable manner.  
Each human subject wore an exoskeleton robot and carried out two experiments at a constant speed without weight. The first kind of experiment was to walk in different locomotion patterns, which consisted of level-ground walking, stairs ascent and stairs descent. Each locomotion pattern included 10 experimental trials for one human subject and each experiment lasted for about 20 s. The second kind of experiment is that each experimenter walked from the level ground walking to the other locomotion patterns and vice versa. The second kind of experiment includes 4 transitions. Each transition consisted of 5 experiments for one subject and the experiment lasted for 60 s. The stairs are 1.5 m wide, 40 cm deep and 15 cm high with a dip Angle of about 26\({}^{\circ}\).  
In order to filter out the noise and interference in the data we collected, a Butterworth low-pass filter was used to filter the data with a cut-off frequency of 10 Hz. Finally, we obtained IMUs and FSRs data from the sensor system. Some of the filtered data is shown in Figures 3-5.  
Figure 1: CGA angle definition.  
Figure 2: Plantar pressure sensor.  
Figure 4: Partial stairs ascent data and FSRs data.  
Figure 5: Partial stairs descent data and FSRs data.  
Figure 3: Partial level ground walking data and FSRs data.