The healthcare systems are experiencing heavy workload and high cost caused by ageing population. The assisted monitoring systems for the elderly persons, and persons with chronic diseases, promises great potential to provide them with care and comfort at the privacy of their own homes and as a result help reduce healthcare costs. This requires a monitoring system capable of detecting daily human activities in living spaces. In this work we discuss different challenges to design such a system, present an activity data visualization tool designed to study human activities in a living space and propose a two stage, supervised statistical model for detecting the activities of daily living (ADL) from non-visual sensor data streams. A novel data segmentation is proposed for accurate prediction at the first stage. We present a novel error correction structure for the second stage to boost the accuracy by correcting the misclassification from the first stage.