Human activity recognition through probabilistic data fusion

The increasing availability of smart devices in people's daily lives is constantly driving the design of novel services aimed to support the users by leveraging data provided by sensors embedded in their devices. In this paper, we present a scenario where data generated by wearable devices, such as smartphones and smartwatches, are analyzed to perform Human Activity Recognition (HAR). Given the different nature of the devices, using a single classifier may lead to inconsistent performance, especially for tasks that are semantically complex. Conversely, a distributed approach to activity recognition, where independent classifiers are used on each device, would be more computationally demanding and challenging to maintain. To address these issues, we present a probabilistic data fusion approach to integrate measurements from multiple devices while improving the overall system accuracy. Experiments performed on real data acquired from different devices show the effectiveness of our approach, especially in the recognition of complex activities.