[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117314-en":3,"doc-seo-117314-105":29,"detail-sidebar-cat-0-en-105":90},{"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":4,"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":13,"seo_description":14,"update_tm":27,"read_time":28},117314,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Gait Stride Length Estimation Using Embedded Machine Learning - read online free","Spatiotemporal gait parameters such as stride length are traditionally obtained via instrumented gait analysis, but assessment outside the laboratory requires wearable solutions. Existing IMU-based wearable devices remain resource-constrained, limiting direct on-device machine learning for spatiotemporal estimation. The study builds a multilayer 1D convolutional model using a dataset of 4467 strides from 15 healthy participants, supporting deployment on a microcontroller for tinyML-based wearable gait analysis.","sensors   \nArticle  \nGait Stride Length Estimation Using Embedded Machine Learning  \nJoeri R. Verbiest 1,2,*, Bruno Bonnech±re 2,3, Wim Saeys 4, Patricia Van de Walle 4,5, Steven Truijen 4 and Pieter Meyns 2  \nCitation: Verbiest, J.R.;  \nBonnechère, B.; Saeys, W.;  \nVan de Walle, P.; Truijen, S.; Meyns, P. Gait Stride Length Estimation Using Embedded Machine Learning.  \nSensors 2023, 23, 7166. [https://](https://)[ ](https://)[doi.org/10.3390/s23167166](doi.org/10.3390/s23167166)  \nAcademic Editors: Alessandro Leone and Gabriele Rescio  \nReceived: 6 June 2023  \nRevised: 12 July 2023  \nAccepted: 17 July 2023  \nPublished: 14 August 2023  \nCopyright: © 2023 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 Department of Sciences and Technology, Karel de Grote (KdG) University of Applied Sciences and Arts, 2660 Antwerp, Belgium  \n2 REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium  \n3 Technology-Supported and Data-Driven Rehabilitation, Data Science Institute, Hasselt University, 3590 Diepenbeek, Belgium  \n4 Department of Rehabilitation Sciences and Physiotherapy, MOVANT, Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, 2610 Antwerp, Belgium  \n5 Clinical Gait Analysis Laboratory Antwerp, Heder, Ekeren, 2180 Antwerp, Belgium  \n* [Correspondence: joeri.verbiest@kdg.be](Correspondence: joeri.verbiest@kdg.be)  \nAbstract: Introduction. Spatiotemporal gait parameters, e.g., gait stride length, are measurements that are classically derived from instrumented gait analysis. Today, different solutions are available for gait assessment outside the laboratory, specifically for spatiotemporal gait parameters. Such solutions are wearable devices that comprise an inertial measurement unit (IMU) sensor and a microcontroller (MCU) . However, these existing wearable devices are resource-constrained. They contain a processing unit with limited processing and memory capabilities which limit the use of machine learning to estimate spatiotemporal gait parameters directly on the device. The solution for this limitation is embedded machine learning or tiny machine learning (tinyML) . This study aims to create a machine-learning model for gait stride length estimation deployable on a microcontroller. Materials and Method. Starting from a dataset consisting of 4467 gait strides from 15 healthy people, measured by IMU sensor, and using state-of-the-art machine learning frameworks and machine learning operations (MLOps) tools, a multilayer 1D convolutional float32 and int8 model for gait stride length estimation was developed. Results. The developed float32 model demonstrated a mean accuracy and precision of 0.23 􀀆 4.3 cm, and the int8 model demonstrated a mean accuracy and precision of 0.07 􀀆 4.3 cm. The memory usage for the float32 model was 284.5 kB flash and 31.9 kB RAM. The int8 model memory usage was 91.6 kB flash and 13.6 kB RAM. Both models were able tobe deployed on a Cortex-M4F 64 MHz microcontroller with 1 MB flash memory and 256 kB RAM. Conclusions. This study shows that estimating gait stride length directly on a microcontroller is feasible and demonstrates the potential of embedded machine learning, or tinyML, in designing wearable sensor devices for gait analysis.  \nKeywords: embedded machine learning; tinyML; machine learning; regression; neural network; healthcare; gait analysis; gait stride length; inertial measurement unit; IMU; microcontroller; MCU; wearable sensors  \n1. Introduction  \nSpatiotemporal gait parameters refer to the measurements obtained from the analysis of the walking pattern, which includes the gait stride length parameter. Gait analysis is crucial i","cbCaibUyTINNwcvT","https://ap.wps.com/l/cbCaibUyTINNwcvT","pdf",3297537,1,13,"English","en",105,"# Introduction\n## Spatiotemporal gait parameters and clinical relevance\n## Limitations of laboratory gait analysis\n## Wearable IMU-based solutions and resource constraints\n# Materials and Method\n## Dataset and model development\n## MLOps and model quantization\n# Results\n## Accuracy, precision, and memory usage\n## Deployment feasibility on Cortex-M4F\n# Conclusions\n## Embedded ML/tinyML potential for wearable gait analysis","[{\"question\":\"Why is embedded machine learning needed for gait stride length estimation?\",\"answer\":\"Wearable IMU devices are resource-constrained, which restricts running machine learning directly on the device. Embedded/tinyML enables stride length estimation on a microcontroller despite these limits.\"},{\"question\":\"What dataset and model approach were used in the study?\",\"answer\":\"The model was trained using 4467 gait strides from 15 healthy people measured with an IMU sensor. A multilayer 1D convolutional model was developed with both float32 and int8 variants.\"},{\"question\":\"How accurate are the float32 and int8 models?\",\"answer\":\"The float32 model achieved a mean accuracy and precision of 0.23 ± 4.3 cm, while the int8 model achieved a mean accuracy and precision of 0.07 ± 4.3 cm.\"}]",1785675128,33,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"gait-stride-length-estimation-using-embedded-machine-learning-read-online-free","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/gait-stride-length-estimation-using-embedded-machine-learning-read-online-free/117314/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is embedded machine learning needed for gait stride length estimation?","Question",{"text":74,"@type":75},"Wearable IMU devices are resource-constrained, which restricts running machine learning directly on the device. Embedded/tinyML enables stride length estimation on a microcontroller despite these limits.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What dataset and model approach were used in the study?",{"text":79,"@type":75},"The model was trained using 4467 gait strides from 15 healthy people measured with an IMU sensor. A multilayer 1D convolutional model was developed with both float32 and int8 variants.",{"name":81,"@type":72,"acceptedAnswer":82},"How accurate are the float32 and int8 models?",{"text":83,"@type":75},"The float32 model achieved a mean accuracy and precision of 0.23 ± 4.3 cm, while the int8 model achieved a mean accuracy and precision of 0.07 ± 4.3 cm.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]