[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122105-en":3,"doc-seo-122105-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122105,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","A Machine Learning Model for Predicting Critical Minimum Foot Clearance (MFC) Heights - Research Article","Tripping remains the leading cause of falls, driven in large part by low swing-foot ground clearance at the critical gait event, Minimum Foot Clearance (MFC). This study evaluates whether machine learning can classify MFC into three height sub-categories using toe-off kinematics from the mid-foot. Data were collected from six healthy young adults during treadmill walking, and three models (KNN, Random Forest, XGBoost) were compared for accuracy and runtime. The KNN approach showed strong performance and faster computation suitable for real-time, feed-forward intervention via exoskeletons or assistive devices.","A Machine Learning Model for Predicting Critical Minimum Foot Clearance (MFC) Heights  \nThis is the Published version of the following publication  \nNagano, Hanatsu, Prokofieva, Maria, Asogwa, Clement, Sarashina, Eri and Begg, Rezaul (2024) A Machine Learning Model for Predicting Critical Minimum Foot Clearance (MFC) Heights. Applied Sciences, 14 (15) . ISSN 2076-3417  \nThe publisher’s official version can be found at [https://www.mdpi.com/2076-3417/14/15/6705](https://www.mdpi.com/2076-3417/14/15/6705)[ ](https://www.mdpi.com/2076-3417/14/15/6705)Note that access to this version may require subscription.  \nDownloaded from VU Research Repository [https://vuir.vu.edu.au/49154/](https://vuir.vu.edu.au/49154/)  \napplied sciences  \nArticle  \nA Machine Learning Model for Predicting Critical Minimum Foot Clearance (MFC) Heights  \nHanatsu Nagano 1, *, Maria Prokofieva 1, Clement Ogugua Asogwa 1, Eri Sarashina 2 and Rezaul Begg 1, *  \nCitation: Nagano, H.; Prokofieva, M.; Asogwa, C.O.; Sarashina, E.; Begg, R. A Machine Learning Model for Predicting Critical Minimum Foot Clearance (MFC) Heights. Appl. Sci. 2024, 14, 6705. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/app14156705](10.3390/app14156705)  \nAcademic Editor: Claudio Belvedere  \nReceived: 28 June 2024  \nRevised: 29 July 2024  \nAccepted: 30 July 2024  \nPublished: 1 August 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Institute for Health and Sport (IHES), Victoria University, Melbourne, VIC 8001, Australia  \n2 Graduate School of Comprehensive Human Sciences, Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba 305-8577, Japan  \n* [Correspondence: hanatsu.nagano@vu.edu.au](Correspondence: hanatsu.nagano@vu.edu.au) (H.N.); [rezaul.begg@vu.edu.au](rezaul.begg@vu.edu.au) (R.B.)  \nFeatured Application: The machine learning model predicts Minimum Foot Clearance heights to prevent tripping falls. Integrated into exoskeletons or other assistive devices, it offers realtime interventions for vulnerable populations, enhancing safety with quick and accurate foot clearance adjustments.  \nAbstract: 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 1.5 cm, between 1.5 and 2.0 cm, and higher than 2.0 cm) were predicted to apply machine learning approaches. A total of 18,490 swing phase gait cycles’ data were extracted from six healthy young adults, each walking for 5 min at a constant speed of 4 km/h on a motorized treadmill. K-Nearest Neighbor (KNN), Random Forest, and XGBoost were utilized for prediction based on the data from toe-off for five consecutive frames (0.025 s duration) . Foot kinematics data were obtained from an inertial measurement unit attached to the mid-foot, recording tri-axial linear accelerations and angular velocities of the local coordinate. KNN, Random Forest, and XGBoost achieved 84%, 86%, and 75% accuracy, respectively, in classifying MFC into the three sub-categories with run times of 0.39 s, 13.98 s, and 170.98 s, 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, i.e., toe-off, due to","cbCaitaMbStU92uk","https://ap.wps.com/l/cbCaitaMbStU92uk","pdf",3989909,1,20,"English","en",105,"# Introduction\n## Falls and vulnerable populations\n## Tripping and Minimum Foot Clearance (MFC)\n## Study objective and approach","[{\"question\":\"What is Minimum Foot Clearance (MFC) and why is it important for falls prevention?\",\"answer\":\"MFC is the minimum swing toe height during the mid-swing phase. Low MFC creates a high risk of tripping and forward balance loss, leading to falls.\"},{\"question\":\"How was the dataset for predicting MFC created in the study?\",\"answer\":\"Toe-off kinematics were recorded from six healthy young adults walking on a motorized treadmill. A total of 18,490 swing-phase gait cycles were extracted for model training and evaluation.\"},{\"question\":\"Which machine learning models were tested and how did their accuracies compare?\",\"answer\":\"KNN, Random Forest, and XGBoost were tested using features from toe-off across five consecutive frames. They achieved 84%, 86%, and 75% accuracy, respectively, for classifying MFC into three sub-categories.\"}]","A Machine Learning Model for Predicting Critical Minimum Foot Clearance (MFC) Heights - Research Article | PDF",1785808839,50,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-machine-learning-model-for-predicting-critical-minimum-foot-clearance-mfc-heights-research-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-model-for-predicting-critical-minimum-foot-clearance-mfc-heights-research-article/122105/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is Minimum Foot Clearance (MFC) and why is it important for falls prevention?","Question",{"text":75,"@type":76},"MFC is the minimum swing toe height during the mid-swing phase. Low MFC creates a high risk of tripping and forward balance loss, leading to falls.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the dataset for predicting MFC created in the study?",{"text":80,"@type":76},"Toe-off kinematics were recorded from six healthy young adults walking on a motorized treadmill. A total of 18,490 swing-phase gait cycles were extracted for model training and evaluation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models were tested and how did their accuracies compare?",{"text":84,"@type":76},"KNN, Random Forest, and XGBoost were tested using features from toe-off across five consecutive frames. 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