Most smart home based monitoring / assistive systems that attempt to recognize activities within a smart home are targeted towards living-alone elderly, and stop at providing instantaneous coarse grained information such as room-occupancy or provide specific programmed reminders for taking medication etc. In our work, we target multiple residents, while restricting the use of wearable devices / sensors. In addition we do away with video due to privacy concerns. In this paper we present the design challenges and issues in putting together a sensor network for obtaining micro-context information in multi-person smart spaces. In order to support greater levels of ambient intelligence we support fine grained spatio-temporal data and context acquisition. The architecture is being currently developed into a prototype in a modular fashion for deployment and testing in a variety of environments, and is being concurrently evaluated and tested in real conditions, prior to deployment in a facility for elderly residents with mild cognitive disorder.
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