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A novel approach to continuous and unobtrusive detection of motion patterns in domestic environments is presented. Motion patterns refer to motion primitives which can be detected via presence events emitted by ambient sensors. The approach enables adaption of the system to heterogeneous environments by building upon two pieces of information: a 2D/3D floor plan of the environment and a definition of available sensors. Using this input the system is capable of generating all information required for the monitoring. This minimizes effort for adaption of the system to other environments. A path-planning algorithm is used to automatically detect possible motion patterns and their length within the environment. A generated sensor-graph and finite state machines enable effective processing of sensor events on a common set-top-box. An experiment with 15 participants was conducted. The system is especially suitable for unobtrusive long-term trend analysis in self-selected gait velocity and does not require direct interaction with people monitored.

Motion Pattern Generation and Recognition for Mobility Assessments in Domestic Environments