Towards a generalized regression model for on-body energy prediction from treadmill walking

Abstract

Walking is a commonly available activity to maintain a healthy lifestyle. Accurately tracking and measuring calories expended during walking can improve user feedback and intervention measures. Inertial sensors are a promising measurement tool to achieve this purpose. An important aspect in mapping inertial sensor data to energy expenditure is the question of normalizing across physiological parameters. Common approaches such as weight scaling require validation for each new population. An alternative is to use a hierarchical model to model subject-specific parameters at one level and cross-subject parameters connected by physiological variables at a higher level. In this paper, we evaluate an inertial sensor-based hierarchical model to measure energy expenditure across a target population. We first determine the optimal physiological parameter set to represent data. Weight is the most accurate parameter (p<;0.1) measured as percentage prediction error. We compare the hierarchical model with a subject-specific regression model and weight exponent scaled models. Subject-specific models perform significantly better (p<;0.1 per subject) than weight exponent scaled models at all exponent scales whereas the hierarchical model performed worse than both. We study the effect of personalizing hierarchical models using model results as initial conditions for training subject-specific models with limited training data. Using an informed prior from the hierarchical model produces similar errors to using a subject-specific model with large amounts of training data (p<;0.1 per subject). The results provide evidence that hierarchical modeling is a promising technique for generalized prediction energy expenditure prediction across a target population in a clinical setting.