Pedestrian Dead Reckoning With Wearable Sensors: A Systematic Review

Abstract

Pedestrian Dead Reckoning (PDR) is the process of calculating one's current location by using the previously known position, and advancing that position over time using established or estimated speeds and trajectories (or alternatively stride lengths and directions). PDR plays an important role in modern life, including tracking locations of people and objects whenever GPS is not available. Self-contained PDR systems do not require an infrastructure, thus they can be used for rapid deployment in situations such as search and rescue, disaster relief or medical emergencies. Wearable sensors are often applied in self-contained PDR, but implementation varies in terms of the number, type and location of sensors used. Many algorithms are designed for PDR in order to reduce the error or drift of the final estimate, with various levels of success. There is a lack of comparison between these different methods and this systematic review of PDR for wearable devices provides a comprehensive overview that can inform further design optimizations. The aim of this paper is to assess the quality of all available PDR literature with a focus on wearable sensors. It provides an outline of the state-of-the-art in the field by comparing the accuracy of different sensor layouts and algorithms. Further directions of research are suggested based on these results. This study also highlights the need for more standardised and robust assessment protocols to capture real-world tracking performance of PDR methods.

A Pedestrian Dead Reckoning Method for Head-Mounted Sensors

“…A simple walking pattern (rectangle) was selected in this study in order to minimise any trial variability between subjects and sensor placement. It should be noted that most studies use only a single person in their testing [12] and that this study included multiple subjects. It will be useful for the field to further expand the number of subjects, as well as environmental conditions (e.g., walking surface) during experimental testing to create ever increasing levels of external validity.…”

“…The explicit complementary filter in terms of unit quaternion representation can be expressed in (12), where Ω y is the biased measure of angular velocity by gyroscope, k P is the proportional gain, and k I is the integral gain, p(Ω) = (0, Ω) is a pure quaternion,b denotes gyro bias, ω mes is a correction term, and can be thought of as a non-linear approximation of the error.…”

“…Three healthy adults voluntarily participated in this study, including one female volunteer and two male volunteers. In [12], the majority of PDR studies (66.2%) had only one participant, which made the results somewhat limited. This paper had three subjects and data were collected four times with each device.…”

“…A recent systematic literature review [12] showed that the majority of PDR papers for wearable sensors apply sensors positioned on the foot. This is closely followed by publications that use smartphone based sensors.…”

LOCATE-US: Indoor Positioning for Mobile Devices Using Encoded Ultrasonic Signals, Inertial Sensors and Graph-Matching

“…Nevertheless, the estimation of orientation is not appropriate when the position of the mobile device is not controlled or fixed at a specific position. Hence, the use of an external IMU placed on a fixed position of the person, usually the leg or over the shoe [ 22 ], offers better positioning results than handheld mobile phones, since the pedestrian’s hand often experiences movements not easy to predict [ 23 , 24 , 25 ]. For these reasons, the use of IMUs included in the smartphone was discarded in this work (although it was one of the initial considerations) due to the large errors achieved if the user did not hold the smartphone in a particular position, mainly in trajectories with several turns.…”