Gait analysis is nowadays considered, as a promising contributor towards early detection of cognitive and physical status deterioration when it comes to elderly people. However, the majority of recent efforts on indoor gait analysis methodologies are limited as they only exploit the average walking speed. Applying density based clustering algorithms on indoor location datasets could accelerate context awareness on gait analysis and consequently augment information quality with regard to underlying gait disorders. This work presents the application of DBScan, a well-known algorithm for knowledge discovery, on indoor Kinect location datasets collected in the Active and Healthy Aging Living Lab in the Lab of Medical Physics of the Aristotle University of Thessaloniki. The aim of the paper is to provide evidence that such an approach could effectively discriminate indoor activity High Density Regions which may subsequently be transferred to datasets originated from seniors’ real homes in the light of context aware gait analysis.