[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125880-en":3,"doc-seo-125880-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125880,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders","Step length serves as a key diagnostic and prognostic marker for health and neurological disease, yet existing wearable-based estimation methods remain insufficiently accurate. A machine-learning approach was developed using data from a single lower-back inertial measurement unit worn by 472 young and older participants across neurological conditions, including Parkinson’s disease, and healthy controls. Across over 80,000 steps, performance was strongest for single-step estimates and improved further when averaging ten consecutive steps, meeting a predefined RMSE target. The method produced accurate step-length measures in neurologic patients, supporting continuous monitoring, though further work is needed to reduce errors in specific conditions.","npj | digital medicine Article  \n\n| Published in partnership with Seoul National University Bundang Hospital |  |  |\n| --- | --- | --- |\n| [https://doi.org/10.1038/s41746-024-01136-2](https://doi.org/10.1038/s41746-024-01136-2) |  |  |\n| A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders\u003Cbr> Check for updates |  |  |\n| Assaf Zadka1,2, Neta Rabin1,3, Eran Gazit1, Anat Mirelman1,4, Alice Nieuwboer5, Lynn Rochester6,7, Silvia Del Din 6,7, Elisa Pelosin8,9, Laura Avanzino9,10, Bastiaan R. Bloem11, Ugo Della Croce12, Andrea Cereatti13 & Jeffrey M. Hausdorff  1,4,14,15  |  |  |\n| Step length is an important diagnostic and prognostic measure of health and disease. Wearable devices can estimate step length continuously (e.g., in clinic or real-world settings), however, the accuracy of current estimation methods is not yet optimal. We developed machine-learning models to estimate step length based on data derived from a single lower-back inertial measurement unit worn by 472 young and older adults with different neurological conditions, including Parkinson’s disease and healthy controls. Studying more than 80,000 steps, the best model showed high accuracy for a single step (root mean square error, RMSE = 6.08 cm, ICC(2,1) = 0.89) and higher accuracy when averaged over ten consecutive steps (RMSE = 4.79 cm, ICC(2,1) = 0.93), successfully reaching thepredefined goal of an RMSE below 5 cm (often considered the minimal-clinically-importantdifference) . Combining machine-learning with a single, wearable sensor generates accurate step length measures, even in patients with neurologic disease. Additional research may be needed to further reduce the errors in certain conditions. |  |  |\n| Step length is generally reduced with aging1,2 and among people with neurological disorders3,4. The gait cycle represents a series of movements repeated in a walking pattern5. A step refers to one single step during the cycle, while a stride refers to an entire cycle; since a single stride consists of two steps, step length and stride length are typically highly correlated. Both of these spatial measures of gait, i.e., step length and stride length, are also highly correlated with gait speed6. Indeed, in studies that have grouped the spatial-temporal parameters of gait into different domains (for example, via principal component analyses), it is now relatively common to refer to pace (e.g., step length, gait speed), rhythm (e.g., cadence), and variability (e.g., step-to-step changes in step length)7,8. Alterations in these key spatial- | temporal measures of gait, especially step length, predict adverse health outcomes such as falls, cognitive decline, dementia, morbidity, mortality6,9, 10, and the response to interventions4, 11. Given its importance and ability toreflect aging and the disease stage (e.g., in Parkinson’s disease), step length has also been used as an outcome measure12–15. While large changes in step length can be observed visually, quantitative estimations are required to accurately determine subtle changes in step length over time, monitor the response to therapy, and evaluate disease progression16. This ability can allow for better assessment of changes associated with aging, improve the capacity to objectively detect and track disease, and enhance the ability to quantify the impact of interventions16, 17. |  |\n\n1Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel. 2Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel. 3Department of Industrial Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel. 4Faculty of Medical & Health Sciences and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel. 5Department of Rehabilitation Science, KU Leuven, Neuromotor Rehabilitation Research Group, Leuven, Belgium. 6Translational and Clinica","cbCaifAjjSgWOpHP","https://ap.wps.com/l/cbCaifAjjSgWOpHP","pdf",1705101,6,1,12,"English","en",105,"# Background\n## Importance of step length in health and disease\n## Limits of current estimation methods\n# Methods\n## Data source and wearable sensor setup\n## Machine-learning model for step length\n# Results\n## Estimation accuracy for single steps\n## Accuracy gains from averaging consecutive steps\n# Discussion\n## Clinical relevance of continuous gait monitoring\n## Future research needs","[{\"question\":\"Why is step length important for diagnosing and predicting health outcomes?\",\"answer\":\"Step length reflects aging and disease stage, and alterations predict adverse outcomes such as falls, cognitive decline, dementia, morbidity, and mortality, as well as responses to interventions.\"},{\"question\":\"How does the proposed approach estimate step length continuously?\",\"answer\":\"It uses machine-learning models driven by measurements from a single lower-back inertial measurement unit worn by participants, enabling continuous estimation in real-world or clinical settings.\"},{\"question\":\"What accuracy improvements were achieved in the study?\",\"answer\":\"The best model showed high accuracy for single-step estimates and improved accuracy when averaging across ten consecutive steps, reaching the predefined RMSE goal below 5 cm.\"}]","A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders | 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is step length important for diagnosing and predicting health outcomes?","Question",{"text":77,"@type":78},"Step length reflects aging and disease stage, and alterations predict adverse outcomes such as falls, cognitive decline, dementia, morbidity, and mortality, as well as responses to interventions.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does the proposed approach estimate step length continuously?",{"text":82,"@type":78},"It uses machine-learning models driven by measurements from a single lower-back inertial measurement unit worn by participants, enabling continuous estimation in real-world or clinical settings.",{"name":84,"@type":75,"acceptedAnswer":85},"What accuracy improvements were achieved in the study?",{"text":86,"@type":78},"The best model showed high accuracy for single-step estimates and improved accuracy when averaging across ten consecutive steps, reaching the predefined RMSE goal below 5 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