# Bio-Inspired Real-Time Prediction of Human Locomotion for Exoskeletal Robot Control
## 4 Implementation and Assessment
### Results and Assessment
The adaptability to irregularities could be assessed visually and quantitatively by local irregularity tracking performance observations. The segments considered as irregularities in the assessment dataset were isolated and the predictions with different methods based on the parameters in Table 2 were executed. Some typical local trajectories are shown in Figure 16.  
It can be seen in Figure 16a that a flat curve with zero values from 0 to 0.4 s exists at the very beginning of the predicted curves of Takens' reconstruction methods. Noting that there are no human data (zero), this meant dead time existed. Zero dead time was gained by other methods via offset correction, feedback, and fusion with the Newton-based methods. Figure 16b presents the performance of different methods during transitions. Except for the basic [PERSON] reconstruction method, other methods were able to provide more or less prediction. The NHgo method-predicted curve fluctuated severely. Although the methods fusing the NHgo method, GwOc-KC-NHgo and GwOc-KC-NHgo, were influenced by these fluctuations locally, the fusion provided a better inhibiting effect. As the NHgo method exhibited outstanding prediction capability during periodicity loss, the methods fused with it acquired better prediction ability than without it. Figure 15: The comparison between [PERSON], NHgo, GwOc-KC-NHgo and GwOcFb-KC-NHgo methods (left hip joint data in the sagittal plane). Figure 16c,d were segments where periodicity was invisible, thus it was quite obvious that [PERSON] reconstruction method could hardly provide prediction, while other methods still worked more or less. Quantitatively, the numerical criteria could gave a more objective judgement. Figure 17 compared the \(PR\), \(OPI\), and \(SF\) values between the whole dataset and the dataset during irregularities only. There are three phenomena worth noting in the figure:  
1. The closing up of the assessment measure value gaps between the whole dataset and dataset during irregularities
2. The improvement in the values' changing trends. 3. The signs of differences in assessment measure values between the whole dataset and dataset during irregularities. Compared with the basic Takens' method, the GwOc method closed up the gaps of \(PR\), \(SF\), and \(OPI\) values between the whole dataset and the dataset during irregularities. Also, all the values were improved with a positive trend. Compared with the GwOc method, the GwOcFb method also made the gap even smaller, but the \(SF\) values rose, which brought down the \(OPI\) values. With a similar analysis, the GwOc-KC and GwOc-US methods elevated the smoothness with prediction accuracy degradation, and also narrowed the gaps, which means the synergy improvement was effective in improving the prediction performance. Figure 16: Typical adaptability of different methods to irregularities (left hip joint angle in sagittal plane). (**a**) Dead time during initiation; (**b**) Speed transition from 1 km/h to 2 km/h; (**c**) Stand–walk transition; (**d**) Non-periodic motion during “stopping and leaving the treadmill”. The \(PR\) value difference using the NHgo method with respect to the whole dataset and the dataset of irregularities is negative, which means the prediction accuracy adapted better to irregularities, while for the \(SF\) values the difference is positive, which means the smoothness of predicted trajectories also adapted to irregularities better than periodicity. In contrast, the Takens', GwOc, GwOc-KC, and GwOc-US methods adapted to the whole dataset better. Thus, by fusing the two types of prediction methods, the GwOcFb-KC-NHgo and GwOc-KC-NHgo methods achieved acceptable accuracy and smoothness, and their adaptability to irregularities was improved. The OPI values for irregularities also demonstrated better adaptability of GwOcFb-KC-NHgo and GwOc-KC-NHgo over other methods, performing extremely well with the whole dataset and the irregular segments.