Wearable sensors have great potential for accurate estimation of Energy Expenditure (EE) in daily life. Advances in wearable technology (miniaturization, lower costs), and machine learning techniques as well as recently developed self-monitoring movements, such as the Quantified Self, are facilitating mass adoption. However, EE estimations are affected by a person's body weight (BW). BW is a confounding variable preventing meaningful individual and group comparisons. In this paper we present a machine learning approach for BW normalization and activities clustering. In our approach to activity-specific EE modeling, we adopt a genetic algorithm-based clustering scheme, not only based on accelerometer (ACC) features, but also on allometric coefficients derived from 19 subjects performing a wide set of lifestyle and gym activities. We show that our approach supports making comparisons between individuals performing the same activities independently of BW, while maintaining accuracy in the EE estimate.