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 paper, 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 x 16 cm transducer plate 70 cm in front of different human bodies at different frequencies in the 5-160-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 masses or physical dimensions. This paper focuses on the contactless identification of human body using capacitive sensors in smart home environments.

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 × 16 cm sensor plate determine different capacitance-frequency characteristics over a 5 kHz-160 kHz range, which can be used to identify the person.

A new approach for matrix inversion is introduced. The approach is based on vector representation of multiple-input multiple-output (MIMO) channel matrix, in which the channel matrix is described by a linear combination of channel vectors weighted by their respective system inputs. The MIMO system output is then fed into a bank of adaptive filters, wherein the response of a given adaptive filter is iteratively minimized to match its output to the given system input. In doing so, adaptive filters equalize the impact of respective channel vectors on the MIMO channel output, while simultaneously orthogonalizing themselves from all other channel vectors, forming the channel matrix inverse. The method demonstrates satisfactory convergence and accuracy in Monte Carlo simulations conducted with varying signal-to-noise ratios (SNRs) and matrix conditioning scenarios. The suggested approach, by virtue of its adaptable characteristics, can also be employed for time-varying linear equation systems.