A low-complexity approach for visible light positioning and space-resolved human activity recognition

Achieving precise information on the position of a subject without changing the luminaire infrastructure is a big challenge in positioning approaches that rely on visible light positioning. Achieving high positioning accuracy on a centimeter scale is done by implementing complex receiver unit designs or adapting the existing luminaries. In this context, we suggest a visible light positioning based approach that can determine the position of a person in certain areas of a room without the need of lighting infrastructure modifications. With this approach, one can identify the position with the help of the existing luminaires for the obligatory room lighting. The receiver, represented by a RGB sensitive photodiode, is positioned in an optimized way in order to support both the positioning task as well as the comfort of the user. Based on received signal strength measurements in the red, green and blue channels, we achieve the positioning task by a segmentation of the room into different areas corresponding to the respective impinging light and by utilizing machine learning clustering. Our results show the influence of different segmentation strategies and parameters on the number and size of the distinguishable areas inside the room. Then, we demonstrate the achievable accuracy of our solution approach in real world experiments. Our results show that such light-based positioning data can be fused with IMU sensor data for recognizing human activity.