We present a new fall-detection method using a floor sensor based on near-field imaging. The test floor had a resolution of 9 × 16. The shape, size, and magnitude of the patterns are used for classification. A test including 650 events and ten people yielded a sensitivity of 91% and a specificity of 91%.

-We analyze the performance of a novel human tracking system, which uses the electric near field to sense human presence. The positioning accuracy with moving targets is measured using raw observations, observation centroids and Kalman filtered centroids. In addition to this, the multi-target discrimination performance is studied with two people and a Rao-Blackwellized Monte Carlo data association algorithm. A reel-based triangulation system is used as the reference positioning system. The mean positioning error for five test subjects walking at different speeds is 21 centimeters. The discrimination performance is 90% when the distance between the two people is over 0.8 meters. With distance over 1.1 meters the discrimination performance is 99%.