A freely available system for human activity recognition based on a low-cost body area network

Over the last decade, Human Activity Recognition (HAR) has become a vibrant research field in various applications scenarios, ranging from sports, healthcare and well-being to smart cities, smart homes, and industry, mainly due to the widespread availability of devices as smartphones, smartwatches, and wearables. A key ingredient for sophisticated HAR systems is represented by the availability of high-quality datasets. These are generally gathered by dedicated Body Area Networks (BANs), and further elaborated through machine learning and deep learning algorithms. Thus, the BAN design plays a central role in such a context, where the main challenges are related to easiness of use, costs and energy constraints of their components. In this context, our paper presents a highly configurable HAR system, based on a low-cost and easy-to-use BAN. The system includes a CNN-based algorithm validated over a dataset, collected through the proposed BAN, on 12 persons performing 7 different human activities.