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.