Abstract
Gait assessment is fundamental for the evaluation of mobility. The 10-meter walk test is an established measure of gait speed, yet its simplicity in administration contrasts with the substantial wealth of biomechanical information that is unreported when it is conducted in a conventional manner. Integrating motion capture technology into the 10-meter walk test elevates gait assessment into a high-definition, granular analysis. This Data Descriptor presents the only large-scale, fast-gait 10-meter walk test dataset from Southeast Asia, comprising 100 healthy older adults (43 males and 57 females, aged 50–80 years). In addition, a five-year follow-up model of fall risk in these participants is reported. The dataset is deposited in DR-NTU (Nanyang Technological University Research Data Repository, powered by Dataverse) (https://doi.org/10.21979/N9/3Z2N2Z) and represents a unique normative resource with high potential for reuse in both clinical and research contexts.
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Data availability
The dataset is available from the NTU data repository, DR-NTU, at https://doi.org/10.21979/N9/3Z2N2Z and is released under a Creative Commons Attribution 4.0 (CC BY 4.0) license. Additional information regarding the repository’s data usage agreement is provided in the Supplementary Materials (Appendix).
Code availability
The software tools used in the processing have been described in the Methods section under the Data processing and Data overview. No custom developed code was used in curation and validation of this dataset.
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Acknowledgements
We would like to acknowledge and thank all staff members who significantly contributed to the collection, curation, and management of the Ability Data project, and Isabella Bisio Sole for her support with the technical validation. Open access funding support was provided by the Rehabilitation Research Institute of Singapore (Grant ID: 021099-00001), a tripartite collaboration between the Nanyang Technological University (NTU), the Agency for Science, Technology and Research (A∗STAR), and NHG Health. ChatGPT was used to generate the forest plot based on the data provided, which was subsequently cross-referenced in a statistical software. ChatGPT was also used to assist with language editing and drafting responses to reviewer and editor comments. The authors reviewed and validated all outputs. The final submitted manuscript was screened for similarity using iThenticate.
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All authors contributed to the conceptualization and design of the study. Data Curation – O.R., T.L.W., L.S.L., A.S. Formal Analysis – O.R., T.L.W., A.S. Funding –W.T.A. Investigation – O.R., T.L.W., L.S.L., I.O.T., A.S., P.K. Methodology – O.R., P.C.G., L.S.L., A.S., P.K., K.C., W.T.A. Project Administration – O.R., P.C.G. Resources – A.S., W.T.A. Software – T.L.W. Supervision – K.C., W.T.A., B.Y.T. Validation – O.R., T.L.W., L.X., I.O.T. Visualization – O.R., P.C.G., T.L.W. Writing – original draft – O.R., P.C.G., A.K.K.P. Writing – review & editing – O.R., P.C.G., A.K.K.P., T.L.W., L.S.L., L.X., I.O.T., A.S., P.K., K.C., W.T.A., B.Y.T. All authors approved the final version submitted to the journal and have agreed to be personally accountable for their own contributions.
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Roberts, O., Cruz Gonzalez, P., Kaliya-Perumal, AK. et al. 100 Normative Gait Profiles with 5-year fall tracking: Benchmark Dataset for Southeast Asian Movement Science. Sci Data (2026). https://doi.org/10.1038/s41597-026-07042-4
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DOI: https://doi.org/10.1038/s41597-026-07042-4
