An Efficient Service-based System for Hierarchical Human Activity Sensing.

In this paper, we propose an end-to-end system, based on SEnsing as Service (SEAS) model, which processes continuous mobility data from multiple sensors on the client edge-device by optimizing the on-device processing pipelines. Thus, reducing the cost of data transfer and CPU usage. We also propose a classification algorithm as a part of the system to recognize Activities of Daily Living (ADL).