Asogwa, C.O.; Nagano, H.; Sarashina, E.; Begg, R.; Sankai, Y. A Machine Learning Model for Predicting Critical Minimum Foot Clearance (MFC) Heights. Preprints2023, 2023081076. https://doi.org/10.20944/preprints202308.1076.v1
APA Style
Asogwa, C.O., Nagano, H., Sarashina, E., Begg, R., & Sankai, Y. (2023). A Machine Learning Model for Predicting Critical Minimum Foot Clearance (MFC) Heights. Preprints. https://doi.org/10.20944/preprints202308.1076.v1
Chicago/Turabian Style
Asogwa, C.O., Rezaul Begg and Yoshiyuki Sankai. 2023 "A Machine Learning Model for Predicting Critical Minimum Foot Clearance (MFC) Heights" Preprints. https://doi.org/10.20944/preprints202308.1076.v1
Abstract
Tripping is the largest cause of falls and low swing foot ground clearance during the mid-swing phase, particularly at the critical gait event known as Minimum Foot Clearance (MFC) is the major risk factor for tripping-related falls. Intervention strategies to increase MFC height can be effective if applied in real-time based on feed-forward prediction. The current study investigated the capability of machine learning models to classify the MFC into various categories using toe-off kinematics data. Specifically, three MFC sub-categories (less than 1.5cm, between 1.5-2.0cm and higher than 2.0cm) were predicted applying machine learning approaches. A total of 18,490 swing phase gait cycles’ data were extracted from six healthy young adults, each walking for 5-minutes at a constant speed of 4km/h on a motorised treadmill. Both K-Nearest Neighbour (KNN) and Random-Forest were utilised for prediction based on the data from toe-off for five consecutive frames (0.025s duration). Foot kinematics data were obtained from inertial measurement unit attached to the mid-foot, recording tri-axial linear accelerations and angular velocities of the local coordinate. KNN and Random-Forest achieved 84% and 86% accuracy, respectively, in classifying MFC into the three sub-categories with run time of 0.39 seconds and 13.98 seconds respectively. The KNN-based model was found to be more effective if incorporated into an active exoskeleton as the intelligent system to control MFC based on the preceding gait event, toe-off due to its quicker computation time. The machine learning based prediction model shows promise for the prediction of critical MFC data indicating higher tripping risk.
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
