[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127795-en":3,"doc-seo-127795-105":30,"detail-sidebar-cat-0-en-105":92},{"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},127795,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning - Research","Hereditary spastic paraplegias (HSPs) lead to gait impairments that increase the risk of stumbling or falling, driven biomechanically by reduced ankle motion and limited foot clearance during walking. Established clinical rating scales are clinician-dependent and do not continuously quantify foot elevation dysfunction while patients walk, motivating the need for digital, disease-specific biomarkers. This study develops and evaluates wearable-sensor machine learning classifiers to objectively detect reduced dorsiflexion and clearance and to classify symptom severity.","Ollenschläger et al.  \nOrphanet Journal of Rare Diseases (2023) 18:249 [https://doi.org/10.1186/s13023-023-02854-8](https://doi.org/10.1186/s13023-023-02854-8)  \nOrphanet Journal of Rare Diseases  \n RESEARCH Open Access  \nAutomated assessment of foot elevation   in adults with hereditary spastic paraplegia using inertial measurements and machine learning  \nMalte Ollenschläger1,2* , Patrick Höfner2, Martin Ullrich2, Felix Kluge2, Teresa Greinwalder1, Evelyn Loris 1, Martin Regensburger 1,3, Bjoern M. Eskofier2, Jürgen Winkler1,3 and Heiko Gaßner1,4  \nAbstract  \nBackground Hereditary spastic paraplegias (HSPs) cause characteristic gait impairment leading to an increased risk of stumbling or even falling. Biomechanically, gait deficits are characterized by reduced ranges of motion in lower body joints, limiting foot clearance and ankle range of motion. To date, there is no standardized approach to continuously and objectively track the degree of dysfunction in foot elevation since established clinical rating scales require an experienced investigator and are considered to be rather subjective. Therefore, digital disease-specific biomarkers for foot elevation are needed.  \nMethods This study investigated the performance of machine learning classifiers for the automated detection and classification of reduced foot dorsiflexion and clearance using wearable sensors. Wearable inertial sensors were used to record gait patterns of 50 patients during standardized 4 10 m walking tests at the hospital. Three movement disorder specialists independently annotated symptom severity. The majority vote of these annotations and the wearable sensor data were used to train and evaluate machine learning classifiers in a nested cross-validation scheme.  \nResults The results showed that automated detection of reduced range of motion and foot clearance was possible with an accuracy of 87% . This accuracy is in the range of individual annotators, reaching an average accuracy of 88% compared to the ground truth majority vote. For classifying symptom severity, the algorithm reached an accuracy of 74% .  \nConclusion Here, we show that the present wearable gait analysis system is able to objectively assess foot elevation patterns in HSP. Future studies will aim to improve the granularity for continuous tracking of disease severity and monitoring therapy response of HSP patients in a real-world environment.  \nKeywords Gait analysis, Wearable sensors, Classification, Range of motion, Motion capture, Muscle spasticity  \n*Correspondence:  \nMalte Ollenschläger  \n[malte.ollenschlaeger@fau.de](malte.ollenschlaeger@fau.de)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[. The Creative Commons Public Domain Dedication waiver (](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[http://creativeco](http://creativeco)[mmons.org/publicdomain/zero/1.0/](mmons.org/publicdomain/zero/1.0/)) applies to the data made available in this article, ","cbCaiicL8EkyRi10","https://ap.wps.com/l/cbCaiicL8EkyRi10","pdf",1643107,1,9,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Background","[{\"question\":\"Why is a digital biomarker for foot elevation needed in HSP?\",\"answer\":\"Clinical rating scales require an experienced investigator and are relatively subjective, and they do not continuously quantify foot elevation or foot clearance during walking. This leaves important information about stumbling/falling risk unmeasured.\"},{\"question\":\"How does the study collect data for automated assessment?\",\"answer\":\"Wearable inertial sensors record gait patterns from 50 patients during standardized 4×10 m walking tests in a hospital setting. Three movement disorder specialists independently annotate symptom severity.\"},{\"question\":\"How accurate is the machine learning approach for detecting foot elevation problems and severity?\",\"answer\":\"Automated detection of reduced range of motion and foot clearance achieved 87% accuracy, comparable to individual annotators and averaging 88% against the ground-truth majority vote. Symptom severity classification reached 74% accuracy.\"}]","Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning - Research | PDF",1785941755,23,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"automated-assessment-of-foot-elevation-in-adults-with-hereditary-spastic-paraplegia-using-inertial-measurements-and-machine-learning-research","",{"@graph":36,"@context":86},[37,54,69],{"@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/automated-assessment-of-foot-elevation-in-adults-with-hereditary-spastic-paraplegia-using-inertial-measurements-and-machine-learning-research/127795/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is a digital biomarker for foot elevation needed in HSP?","Question",{"text":76,"@type":77},"Clinical rating scales require an experienced investigator and are relatively subjective, and they do not continuously quantify foot elevation or foot clearance during walking. This leaves important information about stumbling/falling risk unmeasured.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the study collect data for automated assessment?",{"text":81,"@type":77},"Wearable inertial sensors record gait patterns from 50 patients during standardized 4×10 m walking tests in a hospital setting. Three movement disorder specialists independently annotate symptom severity.",{"name":83,"@type":74,"acceptedAnswer":84},"How accurate is the machine learning approach for detecting foot elevation problems and severity?",{"text":85,"@type":77},"Automated detection of reduced range of motion and foot clearance achieved 87% accuracy, comparable to individual annotators and averaging 88% against the ground-truth majority vote. Symptom severity classification reached 74% accuracy.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]