Many accelerometer-based energy expenditure estimation algorithms and platforms have been established in recent topical literature, and each boasts a high correlation against the gold standard in energy expenditure measurement, i.e. indirect calorimetry. The aim of this study was to implement a set of these algorithms, run them aU over a common dataset and investigate the strengths and weaknesses associated with each. The algorithms were then ported to a SHIMMER device for a real time, mobile and non-invasive energy expenditure estimation solution. High correlations were found between the accelerometer-regressed energy expenditure estimates and the reference dataset both on a computer and SHIMMER implementation of the algorithms.