# Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals
## 4 Discussion  
In this paper, we investigated the effect of load variation on the accuracy of gait recognition based on sEMG signals. Based on this, we can examine whether an exoskeleton (or prosthesis) control system, which uses myoelectric signals as the control signal, can still work well in the face of multi-load applications when trained as a single load or as parts of loads. As is shown in Table 1, at each speed, the accuracy of gait recognition under different loads fluctuates obviously (V3: 91.94-83.05%, V5: 97.56-91.18%, and V7: 94.62-89.7%, respectively). The load is closely related to muscle activity [34, 35]--i.e., the heavier the load is, the more active the muscle activity is--which more easily leads to muscle fatigue after a period of walking [35], and affects the gait stability and recognition accuracy. Furthermore, due to inertia in walking, a heavier load produces more forward and lateral pull on the human body [23], which increases instability in the gait and reduces the accuracy of gait recognition. Our study also shows that the load variation significantly affects the accuracy of gait recognition when the load is in the L0, L20, L30, and L40 range (\(p<0.05\)), but no significant effect is found when the load variation in the L0, L20, and L30 range (\(p>0.05\)). One possible reason is that there exists a threshold in the load level [36]. Below this level, the muscle maintains gait stability and the sEMG signals through self-compensation for self-protection [20, 35], while when the load level is above the threshold, the load aggravates the gait instability and the sEMG signal, which results in a substantial reduction of the gait recognition accuracy. Some existing studies may provide a certain extent of support for the threshold hypothesis. [PERSON] et al. [35] revealed that the risk of lower limb injuries may be effectively reduced with a loading of less than 30% of body  
Figure 6: Accuracy of the gait recognition (%) of the groups at three speeds. The testing data (group 3 at V3, group 5 at V5, group 7 at V7) is from all four loads, the training data is comprised of the data from one load (four kinds), two loads (six kinds), three loads (four kinds), and four loads (one kind) at each speed. The average accuracy of the gait recognition (%) for all the one load, two loads, three loads, and four loads, at each speed are labeled in the picture. weight with a backpack, but when the load is increased to 40%, which can cause a later lower limb muscle burst onset. In order to validate our results, we used the k-NN and SVM algorithms to work with this data separately from the BPNN. The results (Figure 4) revealed that the conclusion where load variation has a significant influence on gait recognition accuracy can still be obtained clearly, although the performances of the two algorithms are slightly weaker than the BPNN overall. To test whether an exoskeleton can play a strong role when faced with various loads in real-time environments, it is possible to examine whether the control system with a sEMG signal as the control signal is still effective when the training load and test load are not equal (inter-loads) [13]. Our study, which is shown in Table 2, suggests that when the training set data and the testing set data for the BPNNs are from the same loads (intra-load), the accuracy of gait recognition at each speed is far superior to the accuracy for when the data is from different loads (inter-loads) (88.6-65.11% at V3, 94.25-71.2% at V5, 92.56-71.96% at V7, and total accuracy 91.81-69.42%, respectively). This further confirms that load variation has a significant effect on gait recognition using a sEMG signal. Therefore, it is safe to say that an exoskeleton (or prosthesis) control system that is trained in a single load is not sufficient in the case of multi-load applications.