HARLE: A SURVEY OF INDOOR INERTIAL POSITIONING SYSTEMS FOR PEDESTRIANS 1283
Step Detection
Step Length
Heading
SHS
Particle Filter
Gyroscopes
INS
Accelerometers
Optical
Pressure
Electromyography (EMG)
Sensors
Position
GPS
Building Maps
Radio Fingerprints
RFID
Ultrasonic Ranging
RF Ranging
Statistical Model
Compass
(Magnetometer)
Barometer
SLAM
WiFi
12
13
14
15
16
18
21
1
2
3
3
4
17
7
6
5
25
8
23
24
10
11
22
19
26
1. [6]
2. [7]
3. [8]
4. [9]–[12]
5. [13]
6. [14]
7. [15]–[19]
8. [13], [16], [17], [19]–[21]
9. [16], [17], [21]
10. [16], [17], [21]
11. [22]–[25]
12. [12], [14]
13. [11], [13], [20], [26]
14. [15]–[18], [21], [27]–[31], [31]
15. [19], [21], [31], [32]
16. [9]–[12], [12], [15], [17], [19], [28], [29], [33]
17. [15], [15], [17], [19], [21], [26]–[29], [31]
18. [13], [19]
19. [32], [34]
20. [10]
21. [12], [19], [20]
22. [16]–[19]
23. [18]
24. [25], [29]
25. [30], [33]
26. [24]
Fig. 2. PDR configurations. System inputs are connected via annotated arrows to ellipses, which represent algorithms and system subunits. Arrow annotations
gi ve a numerical key into the list of literature references on the right..
location systems that improve over time as they learn
about the environment.
Both of these techniques are addressed in more detail later
in this survey. Figure 2 provides a graphical summary of all the
sensors and techniques that are surveyed herein. Sensors are
connected to system subunits (shown in ellipses) by arrows
that represent the flow of data. For example, the arrows
starting at the Accelerometers label represent accelerometry
data that have been used to detect steps; to directly estimate
step lengths; and been combined with gyroscopes or with
gyroscopes and a compass to form an INS. For each flow
of data, the figure gives a set of re ferences to literature that
uses that specific sensor or technique.
B. Ambulation
Many of the systems described here succeed by assuming
ambulatory motion and deriving related invar iants. Ambulation
itself can be characterised by the alternate ‘vaulting’ of the
body over a stiffened leg, with the fall being broken by the
opposing leg. At any given moment at least one foot is in
contact with the ground—there is no flight phase as is found
when running. Instead the gait cycle is usually defined in terms
of the phases occurring at a specific foot: the primary phases
are stance and swing. In its stance phase the foot of interest
is firmly planted on the ground, providing a pivot point over
which to vault. In the swing phase, the foot lifts from behind
the pedestrian and swings through to break the fall and enter
its stance phase.
The transition from stance to swing involves the foot
‘peeling’ from the floor, providing a final push from the
toes. This event goes by many names, but toe-off and push-
off are the most common. The transition back to the stanc e
phase begins with the heel contacting the floor (the heel-strike
or foot-down event) before the foot flattens (the foot-flatten
event). The foot remains flattened until the transition to the
swing begins, and the cycle restarts. The strong periodicity
in the movement coupled with the tendency of humans to
sustain a consistent pace allows for a variety of constraints to
be applied.
III. D
ETECTION OF THE GAIT CYCLE
The first task of an SHS is the identification of steps or
strides within the data. In fact, this is even used in many
INSs, as discussed shortly. At a minimum, these algorithms
must permit for accurate step counting, although many systems
also require accurate step segmentation. We can thus identify
two main algorithm types:
• Stance detection—algorithm s that identify periods of data
throughout which a given foot is planted on the floor.
To do this, the sensor is mounted to the foot. Typically
these are appropriate for step counting but give poor
segmentation output;
• Step cycle detection—algorithms that detect cycles in the
sensor data caused by the repetitive motion of walking.
This may involve searching for repeating data patterns or
for repeating events (e.g. the heel-strike). These are well
suited to step segmentation.
Typical stance detection algorithms are threshold-based.
The principle is that the sensor will be static during the stance
phase and the inertial sensors should report a corresponding
lack of activity that thresholding can easily identify. Most
algorithms threshold on the accelerometer magnitude [11],
[17], [35], although angular velocity thresholds have also been
used [21], [32], [36] and combinations have been trialed [27].
Even magnetometer thresholding can give usable stance detec-
tion under some circumstances [19]. In some cases applying