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The advances of electronics provide options for improved monitoring of patients in clinical environment. Medical applications like blood pressure monitoring require precise and wireless altitude measurement in indoor environment. An error of more than a few centimeters bears a risk of mistreatment of patients. Furthermore, user requirements like small form factor, usability and robust operation are important in the medical field. Existing evaluations of indoor positioning systems focus on accuracy analysis of x-and y-coordinates, not on the zcoordinate (altitude). In this paper, we define evaluation criteria for altitude estimation in medical applications. We compare an Ultra-Wide-Band indoor positioning system, an optical Microsoft Kinect camera system and our own development of a wireless barometric sensor against these criteria. We present a comparative measurement setup, results and a final evaluation of the three systems in an indoor environment.

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Wireless medical sensors are an emerging technology. Wireless sensors form networks and are placed in an unknown environment. For indoor scenarios context detection of medical sensors, e.g. removal of sensors from a specific room, is important. Current algorithms for context detection of wireless sensors are based on RF signals, but RF signal propagation and room location show only a weak correlation. Recent approaches with RSSI-measurements are based on prior fingerprinting and therefore costly. In our approach, we equip wireless sensor nodes with a barometric sensor to measure pressure disturbances that occur, when doors of rooms are opened or closed. By signal processing of these disturbances our proposed algorithm detects rooms and estimates distances without prior knowledge in an unknown environment. Based on these measurement we automatically build a topology graph representing the room context and distances for indoor environment in a model for buildings. We evaluate our algorithm within a wireless sensor network and show the performance of our solution.