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Improved Pedestrian Dead Reckoning Positioning with Gait Parameter Learning Improved Pedestrian Dead Reckoning Positioning With Gait Parameter Learning Parinaz Kasebzadeh, Carsten Fritsche, Gustaf Hendeby, Fredrik Gunnarsson and Fredrik Gustafsson Linköping University Post Print N.B.: When citing this work, cite the original article. Original Publication: Parinaz Kasebzadeh, Carsten Fritsche, Gustaf Hendeby, Fredrik Gunnarsson and Fredrik Gustafsson, Improved Pedestrian Dead Reckoning Positioning With Gait Parameter Learning, 2016, Proceedings of the 19th International Conference on Information Fusion, (), , 379-385. http:// ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7527913 Copyright: ISIF http://isif.org Postprint available at: Linköping University Electronic Press http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-130174 Improved Pedestrian Dead Reckoning Positioning With Gait Parameter Learning Parinaz Kasebzadeh, Carsten Fritsche, Gustaf Hendeby, Fredrik Gunnarssony, Fredrik Gustafsson Department of Electrical Engineering, Linkoping¨ University, Linkoping,¨ Sweden Email: ffi[email protected] y Ericsson Research, Linkoping,¨ Sweden, Email: [email protected] Abstract—We consider personal navigation systems in devices phones. These systems use gyroscopes to determine the head- equipped with inertial sensors and Global Positioning System ing and accelerometers to estimate gait parameters such as the (GPS), where we propose an improved Pedestrian Dead Reckon- number of steps and step lengths. ing (PDR) algorithm that learns gait parameters in time intervals when position estimates are available, for instance from GPS or For estimating the gait parameters, it is important to detect an indoor positioning system (IPS). A novel filtering approach step occurrences and their length. These gait characteristics is proposed that is able to learn internal gait parameters in depend on individual walking patterns and vary between the PDR algorithm, such as the step length and the step people. Besides, the same person does not have the same gait detection threshold. Our approach is based on a multi-rate in all situations. That is, step length is a time-varying process Kalman filter bank that estimates the gait parameters when position measurements are available, which improves PDR in which depends on the speed and frequency of steps. These are time intervals when the position is not available, for instance the main challenges of using PDR algorithms. when passing from outdoor to indoor environments where IPS is Various systems and algorithms for PNSs have been intro- not available. The effectiveness of the new approach is illustrated duced in the literature. Comparing them shows that for pedes- on several real world experiments. trian navigation technology, PDR using IMUs has attracted the most interest as it imposes no extra cost and does not rely I. INTRODUCTION on additional infrastructure. PNSs can be generally classified This paper applies a Dead Reckoning (DR) principle to based on the location of the installed sensors. The most a Pedestrian Navigation System (PNS) in order to obtain a popular classes are waist-mounted [1], [2], foot-mounted [3], system to locate the mobile user in both indoor or outdoor [4] and hand-held [5]–[8] types. Besides the large class of environments. These systems are gaining increasing interest IMU-based systems, there are also other approaches that use as a tool to improve the localization aspects specifically in other sensors such as e.g. electronic pedometers [9]. indoor-based problems. In these cases, either the accuracy Since body-fixed systems, introduced above, require extra of the GPS is degraded significantly or the signal is totally devices to be produced and mounted, hand-held devices gain inaccessible, due to blocking line-of-sight (LOS) or strong more interest thanks to the rapid development of smartphones. signal attenuation. The application areas of PNSs are many, Reference [5] is one example of using a hand-held device where navigation for blind, helping people suffering from for positioning purposes. It uses an empirical model which is Alzheimer’s, essential services interruptions, emergency co- based on the accelerometer signal for detecting steps and a ordination, assets tracking, rescue and tracking in big malls back-propagation neural network for step length estimation. are few examples. In [10], a step determination method based on pattern recog- Traditionally, PNSs use micro-electromechanical systems nition is proposed. (MEMS) in order to locate the mobile user when GPS signals Another class of methods used in this context are methods are blocked. Strap-down inertial navigation system (INS) is that use constant, pre-learned gait parameters. As mentioned one example of PNSs that take advantage of MEMS sensors before, the step length depends on the user’s behavioral and for the positioning process. However, these systems are no physical characteristics. In order to take this relation into self-contained navigation systems. The reason comes from account, a few parameters such as weight and height must be the positioning error caused by gyroscope and accelerometer calibrated before starting to measure and performing gait pa- resulting in a rapid drift growth in such systems. rameter estimation. Different studies on this problem propose Pedestrian Dead Reckoning (PDR), that is used in PNSs constant pre-learned parameters in their models. For example, integrates embedded Inertial Measurement Units (IMUs) to in [11] a linear relation between the measured frequency of detect when the user takes footsteps and how the direction steps and a pre-learned constant parameter is proposed for changes between footsteps. IMUs nowadays consist of three- online step length estimation. In [12], the step frequency and axis accelerometers, gyroscopes, and magnetometers, which variation of the acceleration is taken into account together with are inexpensive and light weight. These small-sized sensors a pre-learned constant parameter. with low power consumption are numerously embedded in In this paper, we propose a filtering approach that is able most recent generation of mobile devices, such as smart- to learn gait parameters of the PDR algorithm, such as the step detection threshold and step length. Our approach is Algorithm 1 Step Detection based on a multi-rate Kalman filter bank that estimates the • Input: Three-axis Accelerometer Signal gait parameters when position measurements are available, which improves PDR in time intervals when the position is 1. Compute the norm of the accelerometer signal: unavailable. Moving from outdoor to indoor environments q a = a2 + a2 + a2: where IPS is not available is one example. x y z The rest of the paper is organized as follows: In Sec. II, 2. Band-pass filter the resulting signal using a fourth- a basic pedestrian odometry model is presented together with order Butterworth filter with cut-off frequency an algorithm for step detection. Sec. III then proposes two [0:2; 2:75] Hz. extended pedestrian odometric models, which are embedded in 3. If the filtered signal exceeds a defined threshold h, Sec. IV into a multi-rate KF bank for step detection threshold a step is considered detected. estimation. Sec. V discusses the dead-reckoning improvements 4. Among all sets of accelerations that are larger than by learning the gait parameters on a couple of real-world the threshold, before the signal again drops below the experiments, and Sec. VI finally concludes the work. threshold, the one with the highest value is selected as the step. II. PEDESTRIAN ODOMETRY • Output: Step detections In pedestrian navigation systems, the double-integrating strap-down method suffers from an error in the position estimate. This error propagates and increases quadratically (height and weight) and walking behaviors have participated. with time [13]. Integrating gait parameters could be a solution All of them walked along the same rectangular trajectory. In to this issue. The gait parameters can be utilized in PDR order to have a better classification per person, they were asked algorithms to improve the performance. to walk in a slow pace and count their steps, so that these can Traditionally, PDR algorithms detect gait parameters, such be used as the ground truth. As Fig. 1 shows the threshold as number of detected steps and step length, in order to for each measurement is unique. Furthermore, choosing a too determine the traveled distance. This approach is applied for large threshold will underestimate the number of detected navigation applications in many studies, e.g. [14], [15]. The steps. For instance, the number of detected steps for user 2 basic longitudinal model is given by drops rapidly for thresholds larger than 1 m/s2. ds+1 = ds + L + ns; (1) where d is the total walked distance, s is the step index, L is 400 s User 1 H:180 W:100 the step length and n is noise. Estimating a pedestrian’s step s 350 User 2 H:162 W:62 length and determining a suitable threshold for step detection User 3 H:196 W:110 from IMU accelerometer measurements is discussed in the rest 300 of this section. 250 A. Step Detection 200 The three-axis accelerometer signal contains all the data required to detect the occurrence of a step. The vertical axis, 150 relative to the ground, contains all information corresponding Number of Steps to the step specific peak. However, the orientation of the sensor 100 may cause some disturbances on the vertical signal component. 50 For instance, in the case when the IMU is embedded in 0 hand-held devices such as smartphones. In order
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