## 4 Discussion  
Minimization of tripping risks has been one of the central issues for fall prevention, and providing sufficient swing foot-ground clearance at MFC has been a key consideration while applying an intervention. For the real-time technology as part of the intelligent system, one effective approach is to use feed-forward prediction of upcoming MFC as early as the initiation of the swing phase at toe-off. For healthy young adults, MFC usually takes place approximately around 50% of the swing phase, 0.2 s-0.3 a after toe-off [66]. It can be interpreted that 'prediction and actuation' should, in this example, occur within the mentioned time limit. In the current study, KNN successfully classified MFC into the three categories at 84% accuracy within 0.025 s, suggesting the sufficient reliability and feasibility of our machine learning outputs to be incorporated into intelligent assistive devices. Previous methods for MFC height estimation based on double-integration of vertical acceleration [22] are useful for taking measurements outside the laboratory environment; however, our machine learning-based prediction is the first attempt to devise intelligent active exoskeletons to increase MFC height. We have previously demonstrated that toe-off kinematics can be used to predict MFC timing [36]; in this research we have applied toe-off kinematics for the real-time feedforward prediction of MFC heights. Machine learning approaches are the emerging technique for classification and evaluation of gait patterns based on large data volumes, considered to be the mainstream analytical method in the future and replacing conventional, complex, manual, and customized mathematical programming. The prediction of a future gait event can be incorporated into assistive devices for them to become intelligent real-time systems for augmenting human ambulation. In machine learning use cases, we have employed KNN, Random Forest, and XGBoost for gait classification. All the models successfully classified MFC height into the three subcategories from toe-off information at high accuracies except for XGBoost. Nevertheless, caution is required for machine learning algorithms to provide feedforward control for a powered assistive device in a timely manner. While Random Forest showed better \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Random Forest** & **KNN** & **XGBoost** \\ \hline R1 & 0.87 & 0.84 & 0.73 \\ \hline R2 & 0.83 & 0.71 & 0.73 \\ \hline R3 & 0.97 & 0.92 & 0.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The macro average area under curve (AUC) score for each target class (R1, R2, and R3) on different machine learning models. performance in accuracy, KNN may be the preferred option considering the time taken for prediction to activate assistive devices at MFC based on a preceding toe-off event. High recall and high precision cannot be compromised to ensure correct classification of MFC heights for the populations at critically high and moderate tripping risks, respectively [65]. Further collection of the data is essential in feeding the developed algorithms to improve performance before equipping them into assistive devices for people. It is, however, the primary advantage of the machine learning approach, meaning that the prediction accuracy can basically only improve as data feeding progresses. Our research can be a groundwork for further development for practical equipping with lower limb active exoskeletons. In addition to the essential data feeding, there are some other fundamental concerns to overcome for practical application into assistive devices as intelligent systems. In the current proof-of-concept research, data of healthy young participants were selected to build the algorithms, but prediction of the tripping risk is more useful for vulnerable populations such as older adults, stroke survivors, people with Parkinson's disease, and other pathological conditions.