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This paper presents a practical application called "activity map" to serve as guidance to show ambient intelligence-related contextual information gathered from both humans and their surrounding environments. The activity map utilizes results inferred from a location-aware activity recognition approach to statistically show a resident's possibly interleaved activities along with corresponding location-related contexts. With observations from a variety of multi-modal and non-intrusive wireless sensors, the approach utilizes a Bayesian network fusion engine with inputs from a set of the most informative features extracted from the sensors and applies joint inference to improve the accuracy and consistency of activity and location estimates. Additionally, each feature has to reckon its corresponding reliability factor to control its contribution in case of possible device failure, therefore making the system more tolerant to inevitable disturbance commonly encountered in a cluttered home environment. This mechanism can cope with some inherent sensor limitations and noise interference, thus improving overall robustness and performance. All experiments were conducted in an instrumented living lab and their results demonstrate the effectiveness of the system.