Padati: A Robust Pedestrian Dead Reckoning System on Smartphones

Abstract

Location based services are proving to be the next driving factors for growth in smartphones. While GPS solves the problem of accurate localization in outdoor environments, indoor localization is still an area of active research. Emergence of new generation smartphones with low cost sensors, have provided an effective way of indoor localization by pedestrian dead reckoning (PDR). We propose a robust mechanism for detecting the step of a person and estimating his step length. Our system is independent of the location and orientation of the device. Our system is shown to perform 45% better than the traditional PDR systems proposed in prior-art. Another important problem in PDR system is determining the orientation of the mobile device and the direction of user motion. Many systems assume the device to be oriented in the direction of the user motion. Some of the recent systems use accelerometer, magnetometer patterns and PCA to detect the direction of user orientation. We propose a system which uses map matching and particle filtering to determine the direction of user motion. We tabulate our findings on the feasibility of such a system.

EHAAS: Energy Harvesters As A Sensor for Place Recognition on Wearables

“…In this paper, we only consider the techniques that are feasible on ordinary devices and are for room-level localization (the target of this paper) [15]. Examples include a radio-based technique that utilizes WiFi or Bluetooth, a dead reckoning with inertial sensors [22], and acoustic fingerprinting [24].…”

Energy-efficient human activity recognition for self-powered wearable devices

“…Step detection algorithms have been widely used in health monitoring and indoor positioning applications [102,103,104,105,106]. In these applications, steps are usually detected by using the output of an accelerometer.…”

A Survey of Pedestrian Detection in Video

“…Video cameras are cheaper and easier to find than other types of camera. Meanwhile, several other studies using smartphone built-in camera [1]- [3]. Some recent researches have tried to combine several types of input devices as well as to improve the accuracy of detection results, such as the research by Weimer et al [19].…”

“…Research for pedestrian detection is also implemented in the smartphone mobile devices, such as in the paper [2], [3], [87] . Limited computing capability may be solved by the use of appropriate algorithms.…”

“…A neural network algorithm is applied to perform classification. Same method was also applied in [3] but does not use GPS and WIFI function because both require high memory resources. Optimal algorithm is also proposed in [87] in order to optimize the detection process on a smartphone device.…”