46 International Journal of E-Health and Medical Communications, 2(2), 46-67, April-June 2011
Study of Zero Velocity Update
for Both Low- and
High-Speed Human Activities
R. Zhang, University of Freiburg, Germany
M. Loschonsky, University of Freiburg, Germany
L.M. Reindl, University of Freiburg, Germany
ABSTRACT
Previous studies show that inertial sensor-based personal positioning benefited from Zero Velocity Update
(ZUPT) method by resetting the foot speed at every foot step. However, only the solution for normal pedestrian
movement with small velocity like walking was given. This paper presents a novel ZUPT system which can be
used in a wide range of human activities, including walking, running, and stair climbing by using two inertial
measurement unit (IMU) modules. One is attached on the centre of the human body for human activities’ classification and recognition. The other one is mounted on the foot for ZUPT algorithm implementation based
on the result of activities’ recognition. Test cases include stair climbing by walking and running, walking,
fast walking, and running. In all cases, most of the steps are able to be detected and the new ZUPT system
can be successfully implemented.
Keywords:
Classification, Human Activities, Inertial Measurement Unit (IMU), Inertial Sensor, Localization,
Pedestrian Dead Reckoning (PDR), Step Detection, Still Phase, Zero Velocity Update (ZUPT)
1. INTRODUCTION
Although localization is becoming available
to the general public and businesses via widespread use of Global Positioning System (GPS)
receivers, GPS still needs support in urban and
indoor environments, since GPS signal can be
blocked by high buildings, canyons or forests
among others. As indoors localization can not
rely on GPS, it can be a serious problem in certain situations for emergency responders such
DOI: 10.4018/jehmc.2011040104
as fire fighters. There are some non-GPS approaches to track and navigate personal position
(Fischer & Gellersen, 2010) and these systems
normally require external references. (Zhang,
2009) used mobile base-station based wireless
infrastructure to track the moving object with
the help of Kalman filter and Interacting Multiple Mode (IMM). Sippel (2010) used Optical
Laser Tracking System (OLTS) consisting of
the Laser Base Station (LBS) and the Intelligent
Sensor Node (ISN) to obtain the information of
position. Widyawan et al. (2008) showed that a
novel Backtracking Particle Filter (BPF) could
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International Journal of E-Health and Medical Communications, 2(2), 46-67, April-June 2011 47
be combined with different levels of building
plan detail to improve Pedestrian Dead Reckoning (PDR) performance. Butz et al. (2000)
presented a building information and navigation
system based on Palm Pilot Personal Digital
Assistants (PDAs) and a set of strong infrared
transmitters, located throughout a building.
Newman et al. (2001) provided personnel with
Augmented Reality (AR) services using data
from an ultrasonic tracking system. Generally,
these reference-based systems have advantages
of being able to deliver an absolute position and
orientation in real-time and the error will not
grow with time. However, these systems can
only be utilized if the application permits the
installation of those external references ahead
of time. Cinaz and Kenn (2008) used simultaneous localization and mapping techniques
to automatically generate both localization
information and 2D environment maps by
using head-mounted inertial and laser range
sensors. Although no references are needed, the
approach fails when the tracked object locates
on ruins where no architectural constructions
exist to generate map.
Inertial sensor-based localization systems
provide a solution for personal navigation and
positioning by using inertial sensors such as
accelerometer and gyroscope which neither
depend on the environments in which pedestrians are located nor the references. However,
these systems suffer from the sensor drift when
using low cost inertial sensors. As there are no
references available, the position and orientation errors will grow with time. In order to
provide a long term stable orientation solution,
generally, some other sensors, such as magnetometer and pressure sensor, are integrated
with inertial sensors by Kalman filter, which is
used by modern Attitude and Heading Reference Systems (AHRS) (Foxlin, 1996; Luinge
et al, 2004; Roetenberg et al., 2005). However,
large velocity and position errors are still not
able to be efficiently reduced after two times
integrations due to the sensor drift.
In order to overcome the drift error, Zero
Velocity Update (ZUPT) algorithm resetting
the foot speed at every foot step was introduced
(Ojeda & Borenstein, 2007a, 2007b) by using a
foot-mounted sensor module. Although the drift
problem of normal pedestrian movement with
small velocity like walking is able to be solved
if accurate orientation data can be obtained
from AHRS, the same algorithm-setting fails
when operating high-speed movement, since
the durations of the still phase, whose definition
will be given later on, is not capable of being
detected by using the same threshold value.
Foxlin (2005) suggested enlarging the step detection threshold when experiencing high-speed
movement, but no further details were given.
Even if the correct threshold value for detecting
high-speed movement can be determined, how
to switch the threshold values between activities with different velocities is still a problem.
ZUPT algorithm must be informed to change
the threshold values when the operated movement starts to change.
Multiple sensor modules were commonly
used to cope with kinematic analysis of complicate movement. Renaudin et al. (2007) presented
a distributed architecture of sensors for agent,
in which chest-attached sensor module was
used for orientation analysis, both thigh- and
chest-attached sensor modules were used for
posture analysis and leg-attached sensor module was used for gait analysis. Zhu and Zhou
(2004) described a real-time motion tracking
system, which required more than ten sensor
modules for whole body kinematics analysis.
Schepers et al. (2010) developed an Inertial
and Magnetic Measurement System (IMMS)
which used three orthogonal mounted- circular
coils to generate local magnetic field to further
improve the relative position and orientation
accuracies of human motions determined by
inertial sensor modules. Based on these literatures, a feasible solution to extract information
of the movements with different velocities is
to use additional sensor modules. However, the
minimum number of additional sensor modules
required and the position to place the sensor
modules are still unclear.
We found that in the medical field, people
analysed acceleration data from an acceleration senor to detect and classify patients’ daily
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