# Bio-Inspired Real-Time Prediction of Human Locomotion for Exoskeletal Robot Control
## 4 Implementation and Assessment
### Results and Assessment  
The prediction methods listed in Table 1 and evaluated (Figure 7) demonstrated the dataset with respect to an extending prediction horizon from 1 to 20, which was the requirement of exoskeletal robot sensing for control. For \(OPI\) calculation, \(\alpha\) in Equation (16) was chosen as 0.8, which emphasized accuracy over smoothness. The evaluations were performed with the simulation of real-time operation by pushing the data one by one in a time sequence into the last position of a data pool, which was fair for all the methods. Studying the results by comparison the pros and cons of different methods and improving techniques were clear:  
1. It can seen more clearly in Figure 11 that when compared with the original [PERSON] reconstruction method, the \(PR\) and \(SF\) values of Gw and GwOC had significant improvements, especially during the lower prediction horizon range, which meant the Gaussian weighting and offset correction were effective and the assumption of smoothness and consistency of human motion was reasonable. 2. Also, it can be inferred by comparing GwOc, GwOc-KC, and GwOc-US that for prediction accuracy the synergy improvement of united source of history embedding vector generation had a positive effect on smoothness improvement and a negative effect on accuracy. In contrast, the improvement of Kalman fusion with complementary joint prediction played an opposite role. Hence, the two synergy improvements would be chosen based on application preference. 3. Figures 11-13 show that feedback scheme would push up the \(PR\) value and hold back the \(FS\) value. The \(FS\) curves of the methods with feedback scheme deviated a lot from the other methods with the extension of the prediction horizon. This was always true by comparative inspection. Figure 10: Influence of weighted historical embedding vector number (left hip joint data in the sagittal plane). Hence, if prediction accuracy is expected with highest priority, the feedback scheme should be considered. Figure 11: Comparison of prediction performance between the Takens’, Gw, GwOc, GwOcFb, GwOc-KC, and GwOc-US methods (left hip joint data in the sagittal plane). _OPI_: overall performance index. Figure 12: Comparison of the prediction performance of the GwOc-KC, GwOc-US, GwOcFb-KC, and GwOcFb-US methods (left hip joint data in sagittal plane). In Figure 14, initially in the prediction horizon range of 1-3, the [PERSON]'s method with a high-gain observer had a medium accuracy and also adequate smoothness, but after 3, the [PERSON]'s method-based predictions degraded the fastest in both accuracy and smoothness. Meanwhile, for [PERSON] reconstruction-based methods, the curves changed moderately. The NHgo-fused methods, GwOC-US-NHgo and GwOC-US-NHgo, exhibited a slight drop in \(PR\) values compared to the methods without fusion, but the smoothness was more elevated. As a result, their overall performance was increased. Figure 14: Comparison of the prediction performance of the GwOc-KC, GwOc-US, GwOc-KC-NHgo, GwOc-US-NHgo, and NHgo methods (left hip joint data in the sagittal plane). Figure 13: Comparison of the prediction performance of the GwOc-KC-NHgo, GwOc-US-NHgo, GwOc-KC-NHgo, GwOcFb-KC-NHgo, GwOcFb-US-NHgo methods (left hip joint data in the sagittal plane). By observing Figure 15 for short-term prediction such as in steps 1-3, the basic [PERSON] reconstruction method's performance was unsatisfactory, but its \(OPI\) value rose gradually, which meant its relative performance over other methods increased. The \(OPI\) curve with the KC-NHgo method changed gradually from an descending trend to ascending, which meant it was superior to other methods with respect to an extended prediction horizon. Considering the \(OPI\) values with designed weighting, the fully improved method, GwOc-KC-NHgo, was apparently better across the prediction horizon range. The \(OPI\) values are relative criterion and are significantly affected by the weighting value. If smoothness is preferred, the GwOc-KC-NHgo method is superior.