11th Annual Conference of the International FES Society
September 2006 – Zao, Japan
Portable Gait-Event Detection System with Built-in wireless
sensor configuration
Se-Jin KONG 1, Chul-Seung KIM 1, Gwang-Moon EOM 1
1
Biomedical Engineering, Konkuk University 322, Danwoldong, Choongju, Choongbuk, 380-701, Korea
sejin.kong@gmail.com
Abstract
The purpose of this study is to develop a
portable gait-event detection system which is
necessary for the cycle-to-cycle FES
(functional electrical stimulation) control of
locomotion. Proposed gait-event detection
system consists of a signal measurement part
and gait event detection part. To make the
system portable, we made following
modifications from the previous wired
system. That is, 1) to make the system
wireless using Bluetooth communication, 2)
to make the system small-sized and batterypowered by using low power consumption
microprocessor. We also used a video
camera system to get the reference gait
events. The gait-events were detected off-line
at the main computer using ANN(Artificial
Neural Network) of Machine learning
technique. The proposed system showed no
mis-detection of the gait-events of normal
subject and hemiplegia subjects. The
performance of the system was better than
the previous wired-system, probably due to
the reduction of unnatural movement during
gait signal measurement. It is expected that
the result of this study will be useful in the
design of cycle-to-cycle FES controller.
the muscle delay(100~300ms) is the main
problem to solve for the real-time control [3].
Therefore, it is desirable to use the cycle-tocycle feedback control to decide the stimulus
pattern for the following gait cycle based on the
previous gait cycles[3][4].
A rule-based system consisting of if-then
rules [1][5] and also the Fuzzy system [3][4]
were suggested for the gait-event detection.
However, these methods require predetermined knowledgebase and suffer from
intersubject difference [6].
For such cycle-to-cycle control, recognizing
each gait phase as well as the whole gait cycle
is required [3]. This study aims to define a cycl
e of gait in five gait phases, as in Fig. 1 and diff
erentiate gait phases based on five gait events.
event detecting leg
contralateral leg
Heel Strike
(HS)
Weight
Acceptance
Foot Flat
(FF)
Late Swing
Max Keen
Felxion
(MKF)
Heel Off
(HO)
Early
Swing
Toe Off
(TO)
Terminal
Stance
Fig. 1 Phases and events gait cycle
1. INTRODUCTION
Functional electrical stimulation (FES) refers
to the method for reconstructing the motor funct
ion by applying artificial electric stimuli directl
y on the peripheral motor nerves or muscle
bypassing the paralyzed nerve conducting path,
to complement the lost motor function.
To date, most clinically applied FES systems
for restoration of locomotion use a manually
triggered open-loop control method [1]. But this
manual control cannot cope with the non-linear
and time-varying characteristics of muscle [2].
Recently, there are many attempts to the realtime and closed loop control for the FES. But
Data acquisition
Foot switch 1
Foot switch 2
Gyroscope
¥ì-processor
Bluetooth
Bluetooth
main
computer
Video camera
Gait- event learning/detection
Fig. 2 Gait-signal measurement system structure
The purpose of this study is to develop a
portable gait-event detection system which is
necessary for the cycle-to-cycle FES control of
locomotion.
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11th Annual Conference of the International FES Society
September 2006 – Zao, Japan
To obtain the gait phases, we used the foot
switches and the Gyro sensors [7]. We made the
whole system small-sized, wireless, lowpowered and thus portable. Then we applied it
to both normal and disabled people, to verify its
performance.
f-FSR
r-FSR
Gyro
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Gyroscope
(a) Normal
f-FSR
r-FSR
Gyro
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Foot switch 2
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Foot switch 1
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Fig. 3 Attachment of sensors
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2.1. Portable gait signals detection system
3
In the portable data acquisition system (Fig.
2), the low-powered microprocessor (ATmega
8535L, 2.5V~5.5V /5mA~12mA, Atmel)
integrates signals from two foot switches (FSR10kg, Tech storm Inc.) and a gyro sensor (CG16D0, 5V/4mA, NEC/Tokin). And the system
also generates a signal for the synchronization
between sensors and the video camera (VLPD3, 30frame/sec, Sharp). Measured data and
sync. signals are transferred to main computer
using Bluetooth (KW-SA4, 5V/100mA,
Korwin).
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2
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Time(s)
(b) Mild Hemiplegia
f-FSR
r-FSR
Gyro
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2.2. Gait event learning and detection
algorithm using ANN
Time(s)
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Gyro
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Three hemiplegic patients and one normal
subject were examined. As shown in Fig. 3, we
attached the two foot switches and one gyro
sensor.
2
(c) Severe Hemiplegia 1
The Artificial Neural Network (ANN) is used
to detect gait event. ANN is an information
processing algorithm similar to the information
processing method of brain. The detection of
gait events starts from the ANN learning where
the video camera data is used as a reference
signal of the gait events. If learning is finished,
iterative learning number and squared error at
the output layer are displayed, and inter-neuron
weights are stored in text file to be used
afterward for analyzing other data.
After the learning process, ANN can detect
the gait events with on-line or off-line input
data.
2.3. Experimental method
1
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2
1
0
1
2
3
4
Time(s)
(d) Severe Hemiplegia 2
Fig. 4 Sensor signals and Event detection result of
Normal and Hemiplegia subject
Foot-switch 1 and 2 detect a contact of toe
and heel on ground, respectively. And the gyro
sensor measures the angular velocity of the
shank segment on sagittal plane.
– 238 –
11th Annual Conference of the International FES Society
September 2006 – Zao, Japan
Subjects walked about 10m distance at a
comfortable walking speed, and the experiment
repeated three times per subject.
3. RESULT and DISCUSSION
Fig. 4 show sensors data and the recognized
gait events of the normal and hemiplegic
subjects, respectively. The recognized gait
events matched well with the reference signals
from video camera.
Also, Fig. 5 shows the sequence of gait cycle
recognized by ANN. In results of the normal
subject (a) and mild patient (b), the gait phase
processed regularly in the order of Toe Off ĺ
Max Knee Flexion ĺ Heal Strike ĺ Foot Flat
ĺ Heal Off.
But severe hemiplegic subjects do not show a
regular gait pattern, as shown by Fig. 5(c) and
Fig. 5(d). Their gait pattern is different from
the normal one in order, and especially Fig.
5(d) consists of four gait events because the
heal-off and the toe-off occurs simultaneously.
(marked as foot-off)
Toe Off
Max Knee
Flexion
Heal Strike
Foot Flat
Heal Off
(1)
(2)
(3)
(4)
(5)
(a) Normal subject
Toe Off
Max Knee
Flexion
Heal Strike
Foot Flat
Heal Off
(1)
(2)
(3)
(4)
(5)
(b) Mild hemiplegia
Toe Off
Heal Off
Max Knee
Flexion
Toe Strike
Foot Flat
(1)
(2)
(3)
(4)
(5)
(c) Severe hemiplegia 1
Foot Off
Max Knee
Flexion
Heal Strike
Foot Flat
(1)
(2)
(3)
(4)
(d) Severe hemiplegia 2
Fig. 5 Gait phase from sensor signals
In the rule-based system or Fuzzy system
using knowledge base, separate knowledgebases are required to account for the various
gait-event orders and special events, e.g. footoff. However, it is easy to create the event
detection system for each patient with ANN, as
ANN learns such individual characteristics.
Further improvements of the proposed
system regarding the location and durability of
foot-switch are still required. Because foot-
sensors were attached under the sole of a shoe,
the location of foot-switch had not been same
each time and the mechanical property of the
foot-switch could not be maintained as
experiment go on.
To overcome these
limitations, the modification of foot-switch into
the insole-type one is desirable.
Also, it seems necessary to facilitate clinical
application by unifying and minimizing both
the gait event detection system and the
stimulator, because it is difficult to wear them
and walk, in case they are attached separately.
4. CONCLUSION
We developed a portable gait-event detection
system and verified its ability to detect gaitevents in normal and pathological gaits. It was
also confirmed that the sensor combination of
foot switch and Gyro sensor is useful in
detecting the gait events.
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Acknowledgements
This work was supported by a grant of the Korean
Health 21 R&D Project, Ministry of Health &
Welfare, Republic of Korea (02-PJ3-PG6-EV030004).
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