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Abstract—In the case of disasters such as fires, floods and

earthquakes, effective and rapid rescue operations have to be carried out by first responders. Rescue operations inside of large-scale office buildings, shopping malls and public buildings are a major challenge in terms of positioning and guidance. Global navigation satellite systems (GNSS) and terrestrial radio navigation systems such as global positioning system (GPS), wireless local area network (WLAN), ultra-wideband (UWB) and radio frequency identification (RFID) are either not fully operative inside of buildings or require a costly system infrastructure. Hence, this paper deals with autonomous or self-contained and semi-autonomous positioning for pedestrians based on low-cost inertial measurement units (IMU). A novel combination of improved movement recognition or human motion analysis and magnetic field mapping for absolute re-positioning is proposed. This combination allows for a low error of position and the minimization of the IMU-typically drift, respectively. Therefore in this paper several methods for human motion analysis as well as for magnetic field mapping are assessed. Furthermore a number of meaningful results are shown.

Index Terms— Indoor navigation, pedestrian navigation, human motion analysis, magnetic field mapping.

I. INTRODUCTION n comparison with navigation systems based on absolute positioning or position fixing, in the case of relative

positioning or dead reckoning the error of position increases with the passing of time or ideally with the distance travelled. This characteristic of self-contained and inertial measurement unit- (IMU) based navigation systems is their major drawback. Hence, sophisticated methods and algorithms are essential to achieve an acceptable positioning accuracy and a low increase of the error of position, respectively. Therefore a number of promising approaches for autonomous or self-contained and semi-autonomous indoor pedestrian navigation have already been proposed by several research groups [1] [8-19]. However, these prototypes are still at an experimental stage and not fully operative. This is why it is necessary to

Manuscript received November 30, 2009. The authors are with the Institute of Information Technology in Civil

Engineering, Graz University of Technology, 8010 Graz, Austria, (e-mail: gerald.glanzer@tugraz.at).

intensify research and development regarding self-contained navigation systems for first responders.

State-of-the-art pedestrian positioning systems based on low-cost IMUs typically use a complementary extended Kalman filter (EKF) [20] [21] to estimate the IMU’s orientation and zero velocity updates (ZUPT) in combination with numerical integrations to calculate the distances (dXIMU, dYIMU, dZIMU) [1]. In this context the earth’s magnetic and gravitational field are used to stabilize the measured orientation of the IMU. The distances (dXIMU, dYIMU, dZIMU) are calculated using the shortest feasible period of time which is one footstep. Therefore at least one IMU has to be mounted on one of the user’s feet. ZUPTs are triggered by the movement recognition during the stance phase of each footstep or stride. Furthermore IMUs mounted on several parts of the user’s body are an option to improve the positioning accuracy.

Inside of modern buildings made of ferro concrete an error of position or drift is primarily caused by severe disturbances of the earth’s magnetic field. A suitable way to address this

Self-contained Indoor Pedestrian Navigation by Means of Human Motion Analysis and

Magnetic Field Mapping Gerald Glanzer and Ulrich Walder

I

Fig. 1. Block diagram of our movement recognition algorithm. The section for other movement types is indicated as an additional option. Based on the detected movement type, different algorithms produce the signal (Movement). Zero velocity updates (ZUPT) are triggered by the signal (Movement).

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problem might be additional complementary positioning systems based on position fixing or additional building information [2] in combination with map matching [1]. Navigation systems based on position fixing such as assisted-global positioning system (A-GPS), high sensitivity-global positioning system (HS-GPS), wireless local area network (WLAN), ultra-wideband (UWB) and radio frequency identification (RFID) are either not fully operative inside of buildings or require a costly system infrastructure.

Therefore this paper deals with additional pre-mapped magnetic field information rather then with absolute positioning systems or building information. Furthermore a movement recognition based on an improved human motion analysis detects the movement types walking and running. The detection of other movement types is indicated as an additional option.

The paper is organized as follows. The improved human motion analysis of our indoor pedestrian navigation system is presented and assessed in Section 2. In Section 3 we briefly introduce magnetic field mapping and magnetic finger-printing, respectively. A few experimental results based on our improved movement recognition are given in Section 4. Finally, Section 5 concludes the paper.

II. HUMAN MOTION ANALYSIS A basic finite state machine composed of two states or gait

phases (stance, swing) is shown in Fig. 3.a. The detection of these two states is sufficient in the case of the movement type walking. Furthermore the acceleration signal (aZG) is used to calculate the signal (Movement). In Fig. 2.a. and Fig. 2.b. the acceleration signal (aZG), the velocity signals (vXG, vYG, vZG) and the signal (Movement) of an exemplary movement are shown. For this example the movement type is walking. The signal (Movement) which is produced by the movement recognition triggers the ZUPTs. The IMU was mounted on one of the user’s feet.

Fig. 3.b. shows an extended finite state machine composed of four states or gait phases (stance, pre swing, swing, loading response) [3-7]. In addition Fig. 3.c. shows the acceleration signals (aXG, aYG, aZG) and the corresponding gait phases for one footstep or stride. The detection of the gait phases stance, pre swing, swing and loading response is described in [4] and [6] in detail. Hence, signals from all accelerometers and gyroscopes are used to calculate the signal (Movement). In Fig. 2.c. and Fig. 2.d. the acceleration signal (aZG), the velocity signals (vXG, vYG, vZG) and the signal (Movement) of an exemplary movement are shown. For this example the movement types are walking and running. The detection of four states rather than two states is advantageous in the case of the movement type running.

Fig. 1. shows by means of a block diagram the structure of our improved movement recognition algorithm. The section for other movement types is indicated as an additional option. First of all the movement recognition tries to detect the movement type walking. If the detection of the movement type walking failed, the movement type running or no motion

Fig. 2. (a) The acceleration signal (azG) and the signal (Movement) of the movement type walking (b) Three velocity signals (vxG, vyG, vzG) of themovement type walking. (c) The acceleration signal (azG) and the signal (Movement) of the movement types running and walking. (d) Three velocity signals (vxG, vyG, vzG) of the movement types running and walking.

Fig. 2. (a, b) The acceleration signal (aZG), the velocity signals (vXG, vYG, vZG) and the signal (Movement) of an exemplary movement are shown. The movement type is walking. (c, d) The acceleration signal (aZG), the velocity signals (vXG, vYG, vZG) and the signal (Movement) of an exemplary movement are shown. The movement types are walking and running.

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occurred. Hence, the movement type running is assumed if motion (no walking) is detected. Based on the detected movement type, different algorithms produce the signal (Movement). The main difference between these two movement types is the duration of the stance phase. Therefore it is more difficult to detect the stance phase in the case of the movement type running. Furthermore the error of position increases if the majority of the stance phases are of short duration. For that reason the algorithms which produce the signal (Movement) are different for both movement types.

III. MAGNETIC FIELD MAPPING State-of-the-art positioning systems based on low-cost

IMUs typically use the earth’s magnetic field to calculate the IMUs orientation or the rotation referred to the global z-axis (yaw(z), heading). The performance of such a pedestrian navigation system in an outdoor trial as well as a trial inside of a building with severe disturbances of the earth’s magnetic field is assessed in [1]. Disturbances of the earth’s magnetic field usually induce deviations of the heading (yaw(z)) and errors of position, respectively. In the outdoor scenario the error of position referred to the distance traveled equals 0.77 % mean value and 0.10 % standard deviation. Furthermore in the specific indoor scenario with severe disturbances of the earth’s magnetic field this error increases to 2.11 % mean value and 0.95 % standard deviation.

Hence, we carried out measurements of the absolute value of the magnetic flux density during all trails, which is

222222ZLYLXLZGYGXG mmmmmmm ++=++= . (1)

Furthermore we also carried out calculations of the

difference ( ( )zyawΔ ) between two different angles referred to the global z-axis (yaw(z), heading). These headings are determined using only the three magnetometers and using the three accelerometers, the three gyroscopes and the three magnetometers. For this purpose sensor fusion or an EKF is used. Furthermore the difference ( ( )zyawΔ ) equals not the deviation of the measured magnetic field referring to the non-disturbed earth’s magnetic field.

Fig. 4. shows the magnetic fingerprint ( m , ( )zyawΔ ) of a trajectory of an outdoor trial without disturbances of the earth’s magnetic field. In Fig. 5. the magnetic fingerprint ( m , ( )zyawΔ ) of a trajectory of a trial inside of a building with severe disturbances of the earth’s magnetic field is shown. The magnetic fingerprint ( m , ( )zyawΔ ) of a section of a trajectory inside of a building with severe disturbances of the earth’s magnetic field is shown in Fig. 6. In addition Fig. 7. shows the magnetic fingerprint ( m , ( )zyawΔ ) of the same section of the trajectory. In comparison with Fig. 6. the pedestrian passed through the section in the opposite direction. Both magnetic fingerprints ( m , ( )zyawΔ ) of the section of a trajectory are partially different as well as equal in

Fig. 3. (a) Basic finite state machine composed of two states or gait phases (stance, swing). (b) Extended finite state machine composed of four statesor gait phases (stance, pre swing, swing, loading response). (c) The acceleration signals (aXG, aYG, aZG) and the corresponding gait phases for one footstep or stride. (d) Four trials (1 to 4) with the movement type running. (e)Four trials (1 to 4) with the movement types walking and running.

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their characteristics. Only the absolute value of the magnetic flux density ( m )

which is independent from the orientation of the IMU is nearly equal in both fingerprints. Other values such as the difference ( ( )zyawΔ ) and the three components of the

magnetic flux density ( XGm , YGm , ZGm ) are influenced by errors of the orientation of the IMU. These errors are different for each trial. Unfortunately they depend on the movement types of the pedestrian, the velocity of the pedestrian and other parameters.

IV. EXPERIMENTAL RESULTS In order to assess the performance of our improved

movement recognition, several trials inside of a building with severe disturbances of the earth’s magnetic field were carried out. In this context the movement recognition was evaluated for the movement types walking and running.

In Fig. 3.d. one reference trial (5) with the movement type walking and four trials (1 to 4) with the movement type running are shown. In addition Fig. 3.e. shows the same reference trial (5) with the movement type walking and four trials (1 to 4) with the movement types walking and running. The sections of the trajectories of the trails with the movement type running are marked by arrows. The trajectory of the reference trial starts and ends at the position (14, 4). All other trajectories start and end at the position (0, 0).

The positioning accuracy of the system in the case of the movement types walking and running is lower compared to the movement type walking. Furthermore the accuracy of the system in the case of the movement type running is lower compared to the movement types walking and running. Various starting positions induce different initial headings (yaw(z)) or deviations of the initial headings, if disturbances of the earth’s magnetic field are existing. Furthermore deviations of the headings induce drifts and errors of position, respectively.

V. CONCLUSION In this paper we presented and assessed several methods for

self-contained indoor pedestrian navigation. These methods are based on human motion analysis and magnetic field

mapping or magnetic fingerprinting. Severe disturbances of the earth’s magnetic field inside of buildings as well as particular movement types such as running increase the errors of position without any additional information and methods. Different to other solutions we proposed an improved movement recognition. Furthermore magnetic field mapping or magnetic fingerprinting might be useable for absolute re-positioning. Hence, absolute re-positioning based on disturbed magnetic field information requires novel methods. Unfortunately only the absolute value of the magnetic flux density seems to be useable for magnetic fingerprinting.

Fig. 6. Magnetic fingerprint of a section of a trajectory inside of a building with severe disturbances of the earth’s magnetic field.

Fig. 7. Magnetic fingerprint of the same section of a trajectory inside of a building as in Fig. 6. The pedestrian passed through the section in the opposite direction.

Fig. 5. Magnetic fingerprint of a trajectory of a trial inside of a building with severe disturbances of the earth’s magnetic field.

Fig. 4. Magnetic fingerprint of a trajectory of an outdoor trial without disturbances of the earth’s magnetic field.

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Therefore the usage of building information is more suitable rather than variable pre-mapped magnetic field information.

In addition the usage of building information and magnetic field information might be a promising approach.

Finally, an accurate and robust self-contained indoor pedestrian navigation system provides several advantages for first responders in the case of emergency situations.

ACKNOWLEDGMENT The authors thank the anonymous reviewers for their

helpful suggestions, which have improved the presentation of this paper.

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