Inertial sensors are included in most of the current smart devices and many researchers have identified their ability to be used for analyzing different parameters of human behavior. Among them, the pedestrian’s step length provides very useful information for different applications, such as pedestrian dead reckoning positioning or gait analysis. During the last three decades, many step length estimation methods using inertial sensors have been proposed; however, there is no specific review work that reflects the current state of the art. In this work, we will conduct a systematic and complete review that covers the whole workflow of tasks involved in the design, test and evaluation of step length estimation methods based on inertial sensors. The main conclusion drawn from this review is that the lack of public datasets and standard methodologies to guide the testing and evaluation makes it difficult to compare the different methods in a fair and robust way. Some reflections on how to move forward on this direction have been presented.

Thanks to its ability to provide sub-meter level positioning accuracy, Ultrawideband (UWB) has found wide use in several wireless body area network (WBAN) applications such as ambient assisted living, remote patient management and preventive care, among others. In spite of the attractiveness of UWB, it is not possible to achieve this level of accuracy when the human body obstructs the wireless channel, leading to a bias in the Time of Flight (TOF) measurements, and hence a detection of position errors of several meters. In this paper, a study of how a sub-meter level of accuracy can be achieved after compensating for body shadowing is presented. Using a Particle Filter (PF), we apply UWB ranging error models that take into consideration the body shadowing effect and evaluate them through simulations and extensive measurements. The results show a significant reduction in the median position error of up to 75% and 82% for simulations and experiments, respectively, leading to the achievement of a sub-meter level of localization accuracy.

Over the years, several Ultrawideband (UWB) localization systems have been proposed and evaluated for accurate estimation of the position for pedestrians. However, most of them are evaluated for a particular wearable sensor position; hence, the accuracy obtained is subject to a given wearable sensor position. This paper is focused on studying the effects of body wearable sensor positions i.e., chest, arm, ankle, wrist, thigh, forehead, and hand, on the localization accuracy. According to our results, the forehead and the chest provide the best and worst body sensor location for tracking a pedestrian, respectively. With the wearable sensor at the forehead and chest position, errors lower than 0.35 m (90th percentile) and 4 m can be obtained, respectively. The reason for such a contrast in the performance lies in the fact that, in non-line-of-sight (NLOS) situations, the chest generates the highest multipath of any part of the human body. Thus, the large errors obtained arise due to the signal arriving at the target wearable sensor by multiple reflections from interacting objects in the environment rather than by direct line-of-sight (LOS) or creeping wave propagation mechanism.

In time-of-flight (TOF)-based human ranging systems, target sensors are often mounted on or close to the human body, which may raise non-line-of-sight (NLOS) cases and lead to significant ranging errors depending on the relative position between the body, transmitter (Tx), and receiver (Rx). In recent years, ultrawideband (UWB) has become a very popular technology for human TOF ranging, but its human body interactions have not been studied yet extensively. In this letter, the UWB and human body interaction is explored by the finite-difference time-domain (FDTD) technique, and the obtained E-field strength variation results are validated by means of commercially available UWB kits. Additionally, an UWB-ranging error model with respect to the human body shadowing effect is proposed and evaluated by extensive measurements, i.e., in indoor environments, line-of-sight (LOS) and NLOS are found to be well modeled by Gaussian and Gamma distributions, respectively, while in outdoor fields, LOS and NLOS are both modeled by Gaussian distributions. The main conclusion of this study is that there is a clear pattern between a gain in the E-field strength and TOF ranging errors. It can be established that in a worst-case scenario, a gain of 4–18 dB is observed, which corresponds to about 30–60 cm of TOF ranging errors.

In recent years, several Ultrawideband (UWB) localization systems have already been proposed and evaluated for accurate position estimation of pedestrians. However, most of them are evaluated for a particular wearable sensor position; hence the accuracy obtained is subject to a given wearable sensor position. In this paper, we study the effects of body wearable sensor positions i.e., chest, arm, ankle, wrist, thigh, fore-head, hand, on the localization accuracy. The conclusion drawn is that the fore-head is the best, and the chest is the worst body sensor location for tracking a pedestrian. While the fore-head position is able to set an error lower than 0.35 m (90th percentile), the chest is able to set 4 m. The reason for such a contrast in the performance lies in the fact that in NLOS situations, the chest as an obstacle is larger in size and thickness than any other part of the human body, which the UWB signal needs to overcome to reach the target wearable sensor. And so, the large errors arise due to the signal arriving at the target wearable sensor from reflections of a nearby object or a wall in the environment.