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The potential of the Internet of Things (IoT) has traditionally grown upon the basis of its connectivity and communication capabilities, where low-power devices gather physical data and send them to remote high-performance nodes. However, the Edge Computing paradigm is changing the Cloud-based approach moving the processing and data computation towards the edge, getting the computation closer to the data source. As a consequence, extending intelligence to embedded platforms at the edge involves addressing differently the data processing and the computation techniques to overcome the constraints of the IoT devices. To contribute to this new challenge, we analyze the feasibility of deploying different supervised Machine Learning techniques applied to human activity recognition into two single-board computers, namely a Raspberry Pi 3B+ and a Raspberry Zero W. To that end, we present the …