We demonstrate that a cheap (30USD) small, low power 8x8 thermal sensor array can by itself provide a broad range of information relevant for human activity monitoring in home and office environments. In particular the sensor can track people with an accuracy in the range of 1m (which is sufficient to recognize activity relevant regions), detect the operation mode of various appliances such as toaster, water cooker or egg cooker and actions such as opening a refrigerator, the oven or taking a shower. While there are sensing modalities for each of the above types of information (e.g. current sensors for appliances) the fact that they can all be detected by such a simple sensor is highly relevant for practical activity recognition systems. Compared to vision (or thermal imaging systems) the system has the advantage is being less privacy invasive allowing it for example to monitor bathroom activities (as shown in one of our evaluation scenarios). The paper describes the sensor, the methods used for activity detection and the evaluation.

This work describes the combination of three software frameworks from two different domains: robotics and smart environments. The two robotic frameworks MCA2 and SimVis-3D that have been in use for several years on a multitude of different robotic systems and TinySEP, a modular framework for smart environments were combined to create a win-win-situation for both roboticists and ubiquitous computing researchers. The possibilities and advantages this combination can offer are discussed, especially in situations where mobile robots and smart environments coexist next to each other. This work is concluded by an experiment that shows the feasibility and the strengths of the proposed approach.