MotionAge: A Deep Learning Framework for Biological Age Prediction from Wearable Activity

Yilin Song, Jiaqi Yin, Yirou Hu, Tian Gu
Proceedings of the 11th Machine Learning for Healthcare Conference, PMLR 340:1892-1920, 2026.

Abstract

Population aging is a major public health challenge, driving increasing burden from chronic disease and mortality. To better characterize individual health beyond chronological age, there is growing interest in biological age as a quantitative measure for risk stratification and longitudinal monitoring. Existing biological age models largely rely on laboratory or structured clinical data, limiting scalability and their ability to capture dynamic aspects of health. We propose MotionAge, a deep learning framework that learns a mortality-calibrated biological age directly from high-frequency wearable activity data. The framework combines deep sequence models with a wear-aware modeling strategy that explicitly represents observation reliability, allowing the model to distinguish device non-wear from observed inactivity in noisy and irregularly observed time series. Unlike prior wearable-based methods that predict chronological age or construct unsupervised biomarkers, MotionAge is trained to predict 5-year mortality risk and subsequently maps that risk onto an interpretable age scale. In NHANES accelerometry data, MotionAge achieved stronger mortality discrimination over chronological age and established benchmarks. Within fixed chronological-age bands, higher MotionAge acceleration was associated with lower activity and worse survival, with the largest separation observed among older adults. These results demonstrate that reliability-aware deep learning of wearable time series can yield scalable and interpretable aging phenotypes aligned with clinically relevant health outcomes.

Cite this Paper

BibTeX
@InProceedings{pmlr-v340-song26a, title = {MotionAge: A Deep Learning Framework for Biological Age Prediction from Wearable Activity}, author = {Song, Yilin and Yin, Jiaqi and Hu, Yirou and Gu, Tian}, booktitle = {Proceedings of the 11th Machine Learning for Healthcare Conference}, pages = {1892--1920}, year = {2026}, editor = {Krishnan, Rahul G. and van Amsterdam, Wouter A. C. and Chopra, Sumit and Overgaard, Shauna and Hughes, Michael and Ötleş, Erkin and Shen, Yiqiu and Shanmugam, Divya and Nayan, Madhur and Engelhard, Matthew and Fackler, Jim and Oberst, Michael}, volume = {340}, series = {Proceedings of Machine Learning Research}, month = {12--14 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v340/main/assets/song26a/song26a.pdf}, url = {https://proceedings.mlr.press/v340/song26a.html}, abstract = {Population aging is a major public health challenge, driving increasing burden from chronic disease and mortality. To better characterize individual health beyond chronological age, there is growing interest in biological age as a quantitative measure for risk stratification and longitudinal monitoring. Existing biological age models largely rely on laboratory or structured clinical data, limiting scalability and their ability to capture dynamic aspects of health. We propose MotionAge, a deep learning framework that learns a mortality-calibrated biological age directly from high-frequency wearable activity data. The framework combines deep sequence models with a wear-aware modeling strategy that explicitly represents observation reliability, allowing the model to distinguish device non-wear from observed inactivity in noisy and irregularly observed time series. Unlike prior wearable-based methods that predict chronological age or construct unsupervised biomarkers, MotionAge is trained to predict 5-year mortality risk and subsequently maps that risk onto an interpretable age scale. In NHANES accelerometry data, MotionAge achieved stronger mortality discrimination over chronological age and established benchmarks. Within fixed chronological-age bands, higher MotionAge acceleration was associated with lower activity and worse survival, with the largest separation observed among older adults. These results demonstrate that reliability-aware deep learning of wearable time series can yield scalable and interpretable aging phenotypes aligned with clinically relevant health outcomes.} }
Endnote
%0 Conference Paper %T MotionAge: A Deep Learning Framework for Biological Age Prediction from Wearable Activity %A Yilin Song %A Jiaqi Yin %A Yirou Hu %A Tian Gu %B Proceedings of the 11th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2026 %E Rahul G. Krishnan %E Wouter A. C. van Amsterdam %E Sumit Chopra %E Shauna Overgaard %E Michael Hughes %E Erkin Ötleş %E Yiqiu Shen %E Divya Shanmugam %E Madhur Nayan %E Matthew Engelhard %E Jim Fackler %E Michael Oberst %F pmlr-v340-song26a %I PMLR %P 1892--1920 %U https://proceedings.mlr.press/v340/song26a.html %V 340 %X Population aging is a major public health challenge, driving increasing burden from chronic disease and mortality. To better characterize individual health beyond chronological age, there is growing interest in biological age as a quantitative measure for risk stratification and longitudinal monitoring. Existing biological age models largely rely on laboratory or structured clinical data, limiting scalability and their ability to capture dynamic aspects of health. We propose MotionAge, a deep learning framework that learns a mortality-calibrated biological age directly from high-frequency wearable activity data. The framework combines deep sequence models with a wear-aware modeling strategy that explicitly represents observation reliability, allowing the model to distinguish device non-wear from observed inactivity in noisy and irregularly observed time series. Unlike prior wearable-based methods that predict chronological age or construct unsupervised biomarkers, MotionAge is trained to predict 5-year mortality risk and subsequently maps that risk onto an interpretable age scale. In NHANES accelerometry data, MotionAge achieved stronger mortality discrimination over chronological age and established benchmarks. Within fixed chronological-age bands, higher MotionAge acceleration was associated with lower activity and worse survival, with the largest separation observed among older adults. These results demonstrate that reliability-aware deep learning of wearable time series can yield scalable and interpretable aging phenotypes aligned with clinically relevant health outcomes.
APA
Song, Y., Yin, J., Hu, Y. & Gu, T.. (2026). MotionAge: A Deep Learning Framework for Biological Age Prediction from Wearable Activity. Proceedings of the 11th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 340:1892-1920 Available from https://proceedings.mlr.press/v340/song26a.html.

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