With the proliferation of relatively cheap Internet of Things (IoT) devices, Smart Environments have been highlighted as an example of how the IoT can make our lives easier. Each of these ‘things’ produces data which can work in unison with other devices to create an environment that can react to its users. Machine learning makes use of this data to make inferences about our habits and activities, such as our buying preferences or likely commute destinations. However, this level of human inclusion within the IoT relies on indirect inferences from the usage of these devices or services. Alternatively, Activity Recognition is already a widely researched domain and could provide a more direct way of including humans within this system. With the intended application in the IoT, this research explores the feasibility of using a cost-effective, unobtrusive, single modality ground-based sensor to track subtle direct, and indirect pressure changes. With the subsequent data, a number of machine learning classification approaches are utilised to assess the sensor's performance in activity recognition. The results indicate that accuracy in Activity Recognition classification is generally high and provides a basis for further investigation as an interface to more complex digital systems, such as the IoT.