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 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.affiliation: 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.
authors.forename: Marco
authors.initials: M
authors.lastname: Altini
authors.affiliation: imec The Netherlands, High Tech Campus 31, 5656 AE Eindhoven, The Netherlands.
authors.forename: Pierluigi
authors.initials: P
authors.lastname: Casale
authors.affiliation: imec The Netherlands, High Tech Campus 31, 5656 AE Eindhoven, The Netherlands.
authors.forename: Julien
authors.initials: J
authors.lastname: Penders
authors.affiliation: Chair of Sensor Technology, University of Passau, Innstrasse 41, 94032 Passau, Germany.
authors.forename: Oliver
authors.initials: O
authors.lastname: Amft
mesh_terms: D000328:Adult; D001185:Artificial Intelligence; D015374:Biosensing Techniques; D000072599:Cardiorespiratory Fitness; D005260:Female; D006801:Humans; D008297:Male; D055815:Young Adult
keywords: Bayesian models; Cardiorespiratory fitness; Context recognition; Topic models