Indoor human detection and localization sensors are at the base of many automation and monitoring systems. This work presents an indoor tagless passive human body identification method. It uses a load-mode capacitive sensor to detect the differences in the conductive and dielectric properties of the human body due to differences in body constituency. The experimental results show that four male individuals with similar height but different body mass index (BMI) standing at 70 cm in front of a chest-level 16 cm x 16 cm sensor plate determine different capacitance-frequency characteristics over a 5 kHz-160 kHz range, which can be used to identify the person.

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Although useful for many applications, the practical use of tagless remote human identification is often hampered by privacy, usability, reliability or cost concerns. In this article, we explore the use of capacitive sensors, which appear to address most of these concerns, to identify different persons based on the unique electric and dielectric properties of their bodies given by their specific tissue composition. We present experimental results obtained by measuring the capacitance of a 16 cm×16 cm transducer plate 70 cm in front of different human bodies at different frequencies in the 5 kHz-160 kHz range. The measurements show clearly distinct signatures of capacitance variation with frequency for each person in the experiment, even after accounting for capacitance variations due to different body mass or physical dimensions. This work focuses on the contactless identification of human body using capacitive sensors in smart home environments.

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Accurate indoor person localization is essential for several services, such as assisted living. We introduce a tagless indoor person localization system based on capacitive sensing and localization algorithms that can determine the location with less than 0.2 m average error in a 3 m × 3 m room and has recall and precision better than 70%. We also discuss the effects of various noise types on the measurements and ways to reduce them using filters suitable for on-sensor implementation to lower communication energy consumption. We also compare the performance of several standard localization algorithms in terms of localization error, recall, precision, and accuracy of detection of the movement trajectory.