The purpose of this thesis is to assert the feasibility of classifying when a worker is using a construction power tool with the use of commercial smartwatches. Tiny Machine Learning (TinyML) classification algorithm are proposed, evaluated and tested. Construction is recognized as one of the least productive and most dangerous industries worldwide, and despite governments’ efforts regulating construction sites, workers incidents and fatalities rates are higher than other industries, and are dropping only at slow rates. The sector suffers from a lack of innovation, and new technologies adoption is hindered by technical challenges and a conservative culture. Indeed, processes and productivity are hardly evolving since decades.