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). The results indicate that our proposed system recognizes ADLs with considerable accuracy and flexibility.Clinical relevance— Measurement of Activities of Daily Living has a high correlation with independent living measures for elderly people [1] and post-event rehabilitation where an event may be heart-attack [2], stroke [3], surgical intervention [4], or trauma [5] etc.