For example, heel contact accompanies foot contact impact that can be recorded by force plates, but IMUs measure accelerations that are affected by foot contact impact with the walking surface. [PERSON] et al. [38] installed a single IMU inside the shoe insole to detect both heel contact and toe-off by characterizing particular acceleration patterns identified by time frequency analysis. MFC is more complicated to characterize its distinctive features because it takes place during the mid-swing phase without foot-ground interaction. MFC events can still be effectively detected by characterizing specific foot angles, maximum horizontal velocity (i.e., the transition to zero horizontal acceleration from a positive value), or zero vertical velocity at the lowest swing foot height (i.e., the transition to zero vertical acceleration from a negative value). In addition to gait event detection, measurements of spatial data were undertaken by the application of IMUs. While temporal data can be highly associated with event detection techniques, direct spatial data measurement requires completely different approaches. Double integration is one of the main principles to estimate spatial displacement. Despite proneness to errors due to 'drift', accelerations (m/s\({}^{2}\)) can derive velocities (m/s) and then displacements (m). [PERSON] et al. [39], for example, applied a single IMU on the pelvis to estimate step length with 2-3% errors, utilizing the Kalman filter in combination with double integration. In contrast to displacement estimation in the horizontal plane (i.e., step length), the previous study attempted the MFC height measurement by applying a regression model that incorporates fifteen features from IMUs [32]. Accurate measurement outside the laboratory environment is important, but if assistive devices are to be designed to increase MFC height in real-time to prevent tripping, 'predictability' is necessary. Instead of precise MFC height measurement, hazardous swing foot clearance should be classified in advance so that active actuation can take place in time to assist a wearer with providing further foot clearance to avoid tripping. It is, therefore, meaningful to take a classification approach instead of precise measurement for practical application. The use of machine learning to study MFC height focuses mainly on predictive measurements. For example, [PERSON] et al. [40] used a generalized regression neural network (GRNN) to estimate MFC height using data from an IMU sensor. The GRNN-based MFC height predictions demonstrated a root mean square error (RMSE) of 6.6 mm with nine optimum features during treadmill walking. [PERSON] et al. [41] predicted minimum and maximum toe clearance height with radial basis function network (RBFN); accuracies in both cases still constituted a drawback. Instead, a recent idea was to study gait features by applying deep neural networks to heel contact and toe-off trajectories to predict other gait parameters [42]. Similar studies by [PERSON] et al. [43] used deep convolution neural networks (DCNNs) to predict different types of gaits, which also precluded critical information on MFC heights. A classification of MFC height into categories is necessary to provide the required insight for technological intervention. The goal of this paper is to investigate the use of machine learning to classify MFC heights into categories so that a risky swing foot clearance, if determined in advance, will activate an actuator in time to assist a wearer. Thus, the required algorithm should be capable of quick computation for device actuation. In our current study, we have applied IMUs to record toe-off characteristics described by three-axial accelerations and angular velocities. The objective is to find out whether kinematics data following toe-off could be utilized for the classification of MFC heights into categories (e.g., high, medium, and low).