Marco Altini1, Pierluigi Casale2, Julien Penders2, Oliver Amft3. 1. Signal Processing and Systems, Eindhoven University of Technology, Den Dolech 2, Eindhoven, The Netherlands; Bloom Technologies, Agoralaan Building Abis 2.13, 3590 Diepenbeek, Belgium. Electronic address: altini.marco@gmail.com. 2. imec The Netherlands, High Tech Campus 31, 5656 AE Eindhoven, The Netherlands. 3. Chair of Sensor Technology, University of Passau, Innstrasse 41, 94032 Passau, Germany.
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 activities 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.
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 activities 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.
Authors: Jan Szczegielniak; Krzysztof J Latawiec; Jacek Łuniewski; Rafał Stanisławski; Katarzyna Bogacz; Marcin Krajczy; Marek Rydel Journal: PLoS One Date: 2018-02-09 Impact factor: 3.240