Estimation of Walking Energy Expenditure using a Single Sacrum-Mounted IMU Based on Biomechanically-Informed Machine Learning

Estimation of Walking Energy Expenditure using a Single Sacrum-Mounted IMU Based on Biomechanically-Informed Machine Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Estimation of Walking Energy Expenditure using a Single Sacrum-Mounted IMU Based on Biomechanically-Informed Machine Learning Jinsung Jung, Hyerim Lim, Hyunho Jeong, Sameer Upadhye, Joo H. Kim, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7065154/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Energy expenditure (EE) estimation during walking has significant applications in healthcare, sports science, and rehabilitation, but remains challenging to measure in real-world settings. Existing wearable approaches often require complex multi-sensor systems or extensive training datasets, limiting their practical implementation. We propose a biomechanically-informed approach to estimate EE based on sagittal joint powers of the body segment, derived from a single sacrum-mounted inertial measurement unit (IMU). Scaling relationships between efficiency-weighted segmental joint mechanical power and whole-body EE were first established by regression analysis. Segmental analysis revealed that sagittal-plane joint mechanical power of the body segment, particularly the stance leg strongly correlates with whole-body joint mechanical power (R > 0.9 across subjects and walking speeds). Leveraging this relationship and a lightweight artificial neural network that predicts segmental joint dynamics from an IMU data, the whole-body EE was estimated from the stance-leg sagittal power with efficiency coefficients and the regression-based scale factor. The approach was validated in 13 healthy adults walking at multiple speeds on a treadmill, with ground-truth EE measured via indirect calorimetry. The results demonstrated remarkable consistency both within individuals across speeds and across different subjects (coefficient of variation < 2%), suggesting a robust biomechanical linkage. Furthermore, joint dynamics of the stance leg were accurately estimated from single sacrum-mounted IMU data incorporating a single-leg stance partition and gait speed information. The resulting stance-leg power estimates enabled accurate EE estimation (RMSE 0.69 W/kg) across an independent cohort. This study demonstrates that sagittal-plane joint mechanical power of the body segment particularly the stance leg serves as a reliable biomechanical surrogate for whole-body EE during walking which can be robustly inferred through the efficiency-weighting and regression-scaling. The proposed method offers a simple and practical solution for wearable EE monitoring, with potential applications in clinical rehabilitation, exercise prescription, and daily health tracking. Physical sciences/Engineering Health sciences/Health care Health sciences/Medical research Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 Oct, 2025 Reviews received at journal 01 Oct, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviews received at journal 03 Aug, 2025 Reviewers agreed at journal 20 Jul, 2025 Reviewers invited by journal 18 Jul, 2025 Editor assigned by journal 18 Jul, 2025 Editor invited by journal 09 Jul, 2025 Submission checks completed at journal 09 Jul, 2025 First submitted to journal 09 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7065154","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":488046510,"identity":"f1936284-a26f-4da5-9dd2-1241618e2e93","order_by":0,"name":"Jinsung Jung","email":"","orcid":"","institution":"Korea Advanced Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jinsung","middleName":"","lastName":"Jung","suffix":""},{"id":488046511,"identity":"52952127-82cb-49ca-95be-54a9e3b3f575","order_by":1,"name":"Hyerim Lim","email":"","orcid":"","institution":"Kumoh National Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Hyerim","middleName":"","lastName":"Lim","suffix":""},{"id":488046512,"identity":"192207c0-1e89-4a32-a425-b97cf9155b27","order_by":2,"name":"Hyunho Jeong","email":"","orcid":"","institution":"Korea Advanced Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hyunho","middleName":"","lastName":"Jeong","suffix":""},{"id":488046513,"identity":"d2e186ab-3f66-4a1b-b92d-8c5c9ad54d98","order_by":3,"name":"Sameer Upadhye","email":"","orcid":"","institution":"New York University","correspondingAuthor":false,"prefix":"","firstName":"Sameer","middleName":"","lastName":"Upadhye","suffix":""},{"id":488046514,"identity":"e8fdae20-0b0b-4482-9a69-cfde4824364e","order_by":4,"name":"Joo H. 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Existing wearable approaches often require complex multi-sensor systems or extensive training datasets, limiting their practical implementation.\n We propose a biomechanically-informed approach to estimate EE based on sagittal joint powers of the body segment, derived from a single sacrum-mounted inertial measurement unit (IMU). Scaling relationships between efficiency-weighted segmental joint mechanical power and whole-body EE were first established by regression analysis.\n Segmental analysis revealed that sagittal-plane joint mechanical power of the body segment, particularly the stance leg strongly correlates with whole-body joint mechanical power (R \u003e 0.9 across subjects and walking speeds). Leveraging this relationship and a lightweight artificial neural network that predicts segmental joint dynamics from an IMU data, the whole-body EE was estimated from the stance-leg sagittal power with efficiency coefficients and the regression-based scale factor. The approach was validated in 13 healthy adults walking at multiple speeds on a treadmill, with ground-truth EE measured via indirect calorimetry. The results demonstrated remarkable consistency both within individuals across speeds and across different subjects (coefficient of variation \u003c 2%), suggesting a robust biomechanical linkage. Furthermore, joint dynamics of the stance leg were accurately estimated from single sacrum-mounted IMU data incorporating a single-leg stance partition and gait speed information. The resulting stance-leg power estimates enabled accurate EE estimation (RMSE 0.69 W/kg) across an independent cohort.\n This study demonstrates that sagittal-plane joint mechanical power of the body segment particularly the stance leg serves as a reliable biomechanical surrogate for whole-body EE during walking which can be robustly inferred through the efficiency-weighting and regression-scaling. The proposed method offers a simple and practical solution for wearable EE monitoring, with potential applications in clinical rehabilitation, exercise prescription, and daily health tracking.","manuscriptTitle":"Estimation of Walking Energy Expenditure using a Single Sacrum-Mounted IMU Based on Biomechanically-Informed Machine Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 07:21:29","doi":"10.21203/rs.3.rs-7065154/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-07T05:39:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-01T09:49:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282018774901851662406404238230054656882","date":"2025-09-30T18:39:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70388359803758741548622715865553990265","date":"2025-09-03T19:35:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-03T14:55:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172906734693823142788244610546766953005","date":"2025-07-20T11:26:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-18T11:08:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-18T10:36:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-09T19:05:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-09T04:46:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-09T04:43:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eb0e10f3-e04a-468f-9f65-e851818380de","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51807909,"name":"Physical sciences/Engineering"},{"id":51807910,"name":"Health sciences/Health care"},{"id":51807911,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-01-05T16:06:03+00:00","versionOfRecord":{"articleIdentity":"rs-7065154","link":"https://doi.org/10.1038/s41598-025-28393-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-29 15:58:12","publishedOnDateReadable":"December 29th, 2025"},"versionCreatedAt":"2025-07-22 07:21:29","video":"","vorDoi":"10.1038/s41598-025-28393-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-28393-9","workflowStages":[]},"version":"v1","identity":"rs-7065154","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7065154","identity":"rs-7065154","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}