Cardiorespiratory fitness estimation using wearable sensors: laboratory and free-living analysis of context-specific submaximal heart rates

In this work, we propose to use pattern recognition methods to determine submaximal heart rate (HR) during specific contexts, such as walking at a certain speed, using wearable sensors in free-living, and use context-specific HR to estimate cardiorespiratory fitness (CRF). CRF of 51 participants was assessed by a maximal exertion test (VO2max). Participants wore a combined accelerometer and HR monitor during a laboratory based simulation of activities of daily living and for two weeks in free-living. Anthropometrics, HR while lying down and walking at predefined speeds in laboratory settings were used to estimate CRF. Explained variance (R2) was 0.64 for anthropometrics, and increased up to 0.74 for context-specific HR (0.73 to 0.78 when including fat-free mass). Then, we developed activity recognition and walking speed estimation algorithms to determine the same contexts (i.e. lying down and walking) in free-living. Context-specific HR in free-living was highly correlated with laboratory measurements (Pearson's r = 0.71-0.75). R2 for CRF estimation was 0.65 when anthropometrics were used as predictors, and increased up to 0.77 when including free-living context-specific HR (i.e. HR while walking at 5.5 km/h). R2 varied between 0.73 and 0.80 when including fat-free mass among the predictors. RMSE was reduced from 354.7 ml/min to 281.0 ml/min by the inclusion of context-specific HR parameters (21% error reduction). We conclude that pattern recognition techniques can be used to contextualize HR in free-living and estimated CRF with accuracy comparable to what can be obtained with laboratory measurements of HR response to walking.