This paper presents the shadowing analysis of a body area network (BAN) diversity antenna based on the statistical measurements of the human walking motion. First, the dynamic characteristics of the arm-swing motion were measured using human subjects, and a statistical analysis was then carried out using the measured data to extract useful information for the analysis of a BAN diversity antenna. Second, the analytical results of the shadowing effects of the BAN antenna were shown based on the statistical data of the swing motion. The difference between the typical and the realistic arm-swinging models significantly affected the bit error rate (BER) characteristic of the BAN antenna. To eliminate the shadowing caused by the movement of the arms, a BAN diversity antenna was used. Particular emphasis was placed on the evaluation of the spatial separation of the diversity antennas to attain reduction of the signal-to-noise ratio (SNR) required to achieve a specific BER performance, considering the combined outcome of shadowing and multipath fading unique to BAN antenna systems. We determined that an antenna angle separation of greater than 80° is required to reduce the shadowing effects when the diversity antenna is mounted at the left waist in a symmetrical configuration. Further, an antenna angle separation of 120° is required when the diversity antenna is mounted in an asymmetric configuration.

UWB (Ultra Wide-Band) pulse radar is a promising candidate for surveillance systems designed to prevent crimes and terror-related activities. The high-speed SEABED (Shape Estimation Algorithm based on BST and Extraction of Directly scattered waves) imaging algorithm, is used in the application of UWB pulse radar in fields that require realtime operations. The SEABED algorithm assumes that omni-directional antennas are scanned to observe the scattered electric field in each location. However, for surveillance systems, antenna scanning is impractical because it restricts the setting places of the devices. In this paper, movement of a body is used to replace antenna scanning. The instantaneous velocity of any given motion is an unknown variable that changes as a function of time. A pair of antennas is used to analyze delay time to estimate the unknown motion. We propose a new algorithm to estimate the shape of a human body using data obtained from a human body passing stationary antennas.

This paper presents a new technique for automatically synthesizing human walking motion. In the technique, a set of fundamental motion units called motion primitives is defined and each primitive is modeled statistically from motion capture data using a hidden semi-Markov model (HSMM), which is a hidden Markov model (HMM) with explicit state duration probability distributions. The mean parameter for the probability distribution function of HSMM is assumed to be given by a function of factors that control the walking pace and stride length, and a training algorithm, called factor adaptive training, is derived based on the EM algorithm. A parameter generation algorithm from motion primitive HSMMs with given control factors is also described. Experimental results for generating walking motion are presented when the walking pace and stride length are changed. The results show that the proposing technique can generate smooth and realistic motion, which are not included in the motion capture data, without the need for smoothing or interpolation.