Cardiorespiratory fitness estimation in free-living using wearable sensors

Publication Type Journal Article
Authors Marco Altini, Pierluigi Casale, Julien Penders, Oliver Amft
Title Cardiorespiratory fitness estimation in free-living using wearable sensors
Abstract Objective: In this paper we propose artificial intelligence methods to estimate cardiorespiratory fitness (CRF) in free-living using wearable sensor data.
Methods: Our methods rely on a computational framework able to contextualize heart rate (HR) in free-living, and use context-specific HR as predictor of CRF without need for laboratory tests. In particular, we propose three estimation steps. Initially, we recognize activity primitives using accelerometer and location data. Using topic models, we group activity primitives and derive activity composites. We subsequently rank activity composites, and analyze the relation between ranked activity composites and CRF across individuals. Finally, HR data in specific activity primitives and composites is used as predictor in a hierarchical Bayesian regression model to estimate CRF level from the participant’s habitual behavior in free-living.
Results: We show that by combining activity primitives and activity composites the proposed framework can adapt to the user and context, and outperforms other CRF estimation models, reducing estimation error between 10.3% and 22.6% on a study population of 46 participants.
Conclusions: Our investigation showed that HR can be contextualized in free-living using activity primitives and activity composites and robust CRF estimation in free-living is feasible.
Publication Artificial Intelligence in Medicine
Volume 68
Pages 37–46
Date March 2016
DOI 10.1016/j.artmed.2016.02.002
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Friedrich-Alexander-Universität Erlangen-Nürnberg