# Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals
## 4 Discussion
In addition, in order to further study the effect of load variation on gait recognition accuracy in real-time environments and the possibility of improving gait recognition at the load level, we polled all the data from the four loads as a testing set at each speed to simulate a varied load environment, and we randomly combined the four kinds of data into 15 data sets as the training set to simulate the possible loads that people face in real-time environments. Our results at three different speeds showed the same conclusion: when the training data is from more types of loads, the gait recognition is more accurate as shown in Figure 6. The statistical analysis shows that the number of the kinds of loads in the training set has a substantial influence on the accuracy of gait recognition (\(p<0.001\)), which implies that this is an effective way to improve the accuracy of gait recognition, i.e., by increasing the data from more types of loads in the training set. In our study, we noted that the speed has a significant impact on the accuracy of gait recognition, as we show in Table 1. The total average gait recognition accuracies from the four loads under three speeds are obviously different. Of the four accuracies, the highest value appears at V5 (94.25%), followed by the value at V7 (92.55%), and the lowest value (88.61%) appears at V3. The result is in accordance with [PERSON] et al. [22], whose study revealed that the performance with fast gaits is much better than that in slower gaits. Many researchers have addressed the effect of speed on gait. [PERSON] et al. [37] analyzed the relationship between the speed and the characteristics of the myoelectric signal, and concluded that a natural walking speed can lead to an ideal average sEMG. [PERSON] et al. [38] revealed that a normal comfortable pace is conducive to improving the repeatability of the EMG signal during the gait cycle. This may also explain to a certain extent why the gait recognition accuracy at V5 is the highest, because V5 (5 km/h) is the most commonly used walking speed in daily life. As for why the gait recognition accuracy is the lowest at V3, a possible reason may be that V3 is far below the normal walking speed. Due to the slow speed, people are in a relaxed and casual state in the experiment. Even though the speed of the treadmill is uniform at V3, the speeds of the subjects fluctuate greatly and the stride cannot be kept constant, and so there are great differences in muscle activity while walking [39]. The muscle force of the target muscle cannot maintain an ideal isometric state [40], and the nervous system activity is also unstable [17], which leads to a poor result in gait recognition. Compared to V3, the subject more easily maintains a constant pace and speed on a constant-speed treadmill at V7, so that the muscle action and sEMG signal have better stability, which may be the reason for why this results in a higher accuracy of gait recognition than that at V3. In addition, V7 is faster than the other two speeds so that the muscles at V7 fatigue more easily after a period of walking under the same trial conditions [41], which reduces the accuracy of gait recognition at V7. However, there are some drawbacks to our experiment. For example, we selected healthy people as subjects, who were not really typical of the users of exoskeletons (stroke patients and lower limb hemiplegia patients). Second, we chose only adult males as subjects in the experiment, and we did not consider the effect of gender and age on gait recognition, which does have a significant effect on muscle activity [42; 43] and gait parameters [44; 45]. Thus, future studies will mainly focus on the following two aspects: first, studying subjects who are closer to those who use exoskeletons. Second, more categories of subjects (different age, different gender, and even different regions and races) will be considered to improve the effectiveness of the lower-limb exoskeleton and the prostheses control system in real-time environments.