Activity-Aware Physiological Response Prediction Using Wearable Sensors

Abstract

Prediction of physiological responses can have a number of applications in the health and medical fields. However, this can be a challenging task due to interdependencies between these responses, physical activities, environmental factors and the individual’s mental state. In this work, we focus on forecasting physiological responses in dynamic scenarios where individuals are performing exercises and complex activities of daily life. We minimize the effect of environmental and physiological factors in order to focus on the effect of physical activities. In particular, we focus on forecasting heart rate and respiratory rate due to their relevance in medical and fitness training. We aim to forecast these physiological responses up to 60 s into the future, study the effect of different predictors that incorporate different sensing modalities and different amounts of historical data and analyze the performance of various strategies for prediction. Activity information is incorporated by clustering the data streams and fitting different predictive models per cluster. The effect of clustering is also studied by performing a hierarchical analysis on the clustering parameter, and we observe that activity clustering does improve the performance in our proposed methodology when predicting physiological response across modalities.

Keywords

• [-15]time series analysis
• physiological parameter forecasting
• cluster analysis
• multi-modal data