Ieeepro techno solutions ieee 2014 embedded project on shoe wearable sensors for detecting foot neuropathy
1. On Shoe Wearable Sensors For Detecting Foot Neuropathy
Proceedings of 5th
IRF International Conference, Chennai, 23rd
March. 2014, ISBN: 978-93-82702-67-2
24
ON SHOE WEARABLE SENSORS FOR DETECTING FOOT
NEUROPATHY
1
J.JANE CRISPINO, 2
C.SHEEBA JOICE
1
Department of Electronics and Communication, Saveetha Engineering College, Chennai
2
Professor, Department of ECE, Saveetha Engineering College, Chennai.
Abstract - Foot Neuropathy is a serious medical disorder and can be prevented by the early detection of abnormal pressure
patterns under the foot. By a sending imperceptible vibration through the feet of Parkinson’s disease (PD) patients
significantly improves the damaged nerves and stimulates blood flow. The methods used in clinics are based on complex
motion laboratory settings or simple timing outcomes using stop watches and Timed Up and Go test. The goal of this paper
is to design and build a low-cost foot pressure and foot movement analysis and blood flow stimulation system, embedded
within smart footwear which a patient can wear at any place to monitor his or her foot pressure distribution to identify and
diagnose foot neuropathy as early as possible. To improve the blood flow the smart footwear has a set of miniature vibrating
motors that stimulate the nerves by vibrating in different amplitude that can be configured individually, started and stopped
by the user. The smart footwear will collect data from inertial sensors and periodically transfer this data to the handheld unit
where it will be stored in a memory card for future reference or for an analysis by a doctor.
Keywords – Parkinson Disease, Vibrating motors, Timed up and go, inertial sensors.
I. INTRODUCTION
Parkinson’s disease is a complex neurodegenerative
disease requiring close monitoring and regular
assessment. PD affects a large group of the
worldwide population. It is estimated that worldwide
4 million people are living with Parkinson’s disease.
The disease affects a part of the brain known as the
“Substania Nigra” which controls movement in the
body. Essentially there is a lack of dopamine
(Neurotransmitter) in the brain when Parkinson’s
disease is present. It is a progressive disease that
usually affects people over 50 years of age however
younger people can also be diagnosed with this
disease and this is known as early onset Parkinson’s
disease (PD). There are a wide range of features
associated with the disease making diagnosis
difficult.
Persons with PD may require frequent and time
consuming visits to specialist centers for assessment
and monitoring. Although the main features of PD are
motor related and therefore impact considerably on
daily life many people with the disease suggest
features that are not motor related can affect quality
of life more so than the motor related features. Home-
based monitoring of PD is a laudable aim that has the
potential to reduce the necessity to attend frequent
consultations with specialist personnel in addition to
offering potentially more precise and continuous
monitoring paradigms sensitively assessing disease
progression and medication effects.
The Timed Up and Go (TUG) test is a widely used
clinical test to assess balance, mobility and fall risk in
the elderly and in patients with Parkinson’s disease
(PD). It consists of rising from a chair, walking 3m at
preferred speed, turning around, returning and sitting.
It is simple and easy to perform in the clinic. The
traditional clinical outcome of this test is its total
duration, which is usually measured by a stopwatch.
Gait is a motor task which is particularly sensitive to
ON–OFF changes in PD. When OFF, PD patients
walk slowly with short shuffling steps affecting the
trajectory of foot, reduced stride length, and less
regular cycle, as shown by the increase of inter stride
variability.
II. METHODOLOGY FOR FOOT
NEUROPATHY ANALYZER
The software used is Embedded C and the developing
tool is proteus. The microcontroller used is ARM
CORTEX M3. It consists of Accelerometer and Flexi
force sensor where Accelerometer is a device used to
sense the vibration and force induced by the foot and
Flexi force sensor is used to sense the pressure
distribution under foot.
Fig. 1 Block diagram of foot neuropathy analyzer.
The block diagram of foot neuropathy analyzer is
shown in Fig.1. The pressure distribution in foot is
given as input to inertial sensors which includes
2. On Shoe Wearable Sensors For Detecting Foot Neuropathy
Proceedings of 5th
IRF International Conference, Chennai, 23rd
March. 2014, ISBN: 978-93-82702-67-2
25
accelerometer gyroscope and flexi force sensors.
Accelerometer is used to measure the force applied.
Flexi force sensors are used to measure the pressure
distribution in foot.
Fig. 2 Normal foot and neuropathy foot.
The schematic of healthy foot and foot affected with
neuropathy is shown in Fig. 2. The blood circulation
in healthy foot is normal and there is abnormal blood
flow in affected foot. The sensors are attached under
the foot. Foot neuropathy is a disease with abnormal
pressure in foot. Whenever a difference in foot
pressure is found due to uneven walking an alert is
issued to the hand held device. The vibrating motor
starts to rotate which is used to stimulate blood flow.
The handheld touch screen unit communicates
wirelessly with the foot attached unit and collects
real-time data, stores it in the memory card for
analysis by a doctor at a later time.
The device monitors the user foot movement using a
3-axis MEMS accelerometer and actively looks for
situations leading to foot injuries. Once the system
detects an anomaly in the user's foot pressure
distribution or foot motion, it issues an alert to the
handheld touch screen device. To improve the blood
flow the smart footwear has a set of miniature
vibrating motors that stimulate the nerves by
vibrating in different amplitude that can be
configured individually, started and stopped by the
user using the handheld touch screen unit.
A.Circuit Description Of Foot Detection Unit
The microcontroller used for foot detection and
handheld system is ARM. The foot unit consists of
flexi force sensors, accelerometer, vibrating motors,
transceiver and ARM microcontroller. The flexi force
sensors are connected to AD0, AD1, AD6 pins of the
microcontroller. These pins convert the analog data
into digital data. The motor driver used to run the
vibrating motors is L293D. The input of the motor
driver is connected to port 1 of the microcontroller.
The number of flexi force sensors and vibrating
motors depends on the severity of disease. Here three
flexi force sensors and three vibrating motors are
used. LSM303DLH is the accelerometer used. The
accelerometer measures the force given by the foot in
units of gravity. The accelerometer is connected to
the fifteenth pin of microcontroller. 12MHZ crystal
oscillator is connected to the microcontroller to
generate the frequency required for operation. MIWI
is the wireless transceiver that uses small, low-power
digital radios based on the IEEE 802.15.4 standard.
MIWI communicates with the handheld unit. The
circuit diagram of foot detection system is shown in
Fig. 3.
Fig. 3 Circuit Diagram of foot detection system.
B. Circuit Description of Hand Held Unit
The hand held unit consists of ARM microcontroller,
LCD display and transceiver. The data bus (DB0-
DB7) of the LCD is connected to port 2 of the
microcontroller. The register select of LCD is
connected to port 2. The write and read pins are
connected to port 0 and port 1. Buzzer is connected to
AD0. MIWI transceiver is connected to 47th
pin of
microcontroller. The touch screen controller consists
of serial data in, serial data out and serial clock that
are used to communicate with external peripheral
units. In this system, the foot pressure distribution is
measured by a set flexi force pressure sensors located
on the insole of the shoe. These sensors are based on
force-sensing resistors, whose resistance varies
inversely with the applied force. The footwear unit
measures the pressure sensor outputs and transmits
the information using IEEE 802.15.4 wireless
transceiver to the handheld monitoring unit. The
circuit diagram of hand held device is shown in Fig.
4.
3. On Shoe Wearable Sensors For Detecting Foot Neuropathy
Proceedings of 5th
IRF International Conference, Chennai, 23rd
March. 2014, ISBN: 978-93-82702-67-2
26
Fig. 4 Circuit Diagram of Hand Held Device.
III. RESULT AND DISCUSSIONS
The vibrating motors and sensors are interfaced with
the microcontroller. Here two flexi force sensors are
used. When an anomaly in the foot is detected then
the vibrating motor rotates. The vibrating motor
stimulates the blood. The pressure in the foot is
displayed in the LCD screen. When there is equal
foot pressure then there is no rotation in vibrating
motor. The circuit for foot neuropathy analyzer and
blood flow stimulator for equal pressure is shown in
Fig.5 and Foot pressure detection system when there
is unequal pressure in the foot is shown in Fig. 6.
RA0/AN0
2
RA1/AN1
3
RA2/AN2/VREF-/CVREF
4
RA4/T0CKI/C1OUT
6
RA5/AN4/SS/C2OUT
7
RE0/AN5/RD
8
RE1/AN6/WR
9
RE2/AN7/CS
10
OSC1/CLKIN
13
OSC2/CLKOUT
14
RC1/T1OSI/CCP2
16
RC2/CCP1
17
RC3/SCK/SCL
18
RD0/PSP0
19
RD1/PSP1
20
RB7/PGD
40
RB6/PGC
39
RB5
38
RB4
37
RB3/PGM
36
RB2
35
RB1
34
RB0/INT
33
RD7/PSP7
30
RD6/PSP6
29
RD5/PSP5
28
RD4/PSP4
27
RD3/PSP3
22
RD2/PSP2
21
RC7/RX/DT
26
RC6/TX/CK
25
RC5/SDO
24
RC4/SDI/SDA
23
RA3/AN3/VREF+
5
RC0/T1OSO/T1CKI
15
MCLR/Vpp/THV
1
U1
D714D613D512D411D310D29D18D07
E6RW5RS4
VSS1
VDD2
VEE3
LCD1
LM016L
85%
FSR1
1k
IN1
2
OUT1
3
OUT2
6
OUT3
11
OUT4
14
IN2
7
IN3
10
IN4
15
EN1
1
EN2
9
VS
8
VSS
16
GND GND
U2
L293D
85%
FSR2
1k
A
B
C
D
Fig 5.Circuit for Foot Neuropathy Analyzer And Blood Flow
Stimulator for Equal Pressure
The flexi force sensor 1 is connected to analog
channel AN0 and the flexi force sensor 2 is connected
to analog channel AN1.The flexi force sensors are
denoted by FSR1 and FSR2.L293D is the motor
driver used to drive the vibrating motor. The speed of
the vibrating motor is displayed digitally. In Fig. 5,
the output of the flexi force sensors FSR1 and FSR2
are found to be equal pressure. Both the sensor
outputs are 87. Since there is equal pressure there is
no rotation in the vibrating motor. In Fig. 6, the
output of flexi force sensor 1 is 87 and the output of
flexi force sensor 2 is 84. Since there is a difference
in pressure the vibrating motor starts to rotate which
is driven by motor driver.
RA0/AN0
2
RA1/AN1
3
RA2/AN2/VREF-/CVREF
4
RA4/T0CKI/C1OUT
6
RA5/AN4/SS/C2OUT
7
RE0/AN5/RD
8
RE1/AN6/WR
9
RE2/AN7/CS
10
OSC1/CLKIN
13
OSC2/CLKOUT
14
RC1/T1OSI/CCP2
16
RC2/CCP1
17
RC3/SCK/SCL
18
RD0/PSP0
19
RD1/PSP1
20
RB7/PGD
40
RB6/PGC
39
RB5
38
RB4
37
RB3/PGM
36
RB2
35
RB1
34
RB0/INT
33
RD7/PSP7
30
RD6/PSP6
29
RD5/PSP5
28
RD4/PSP4
27
RD3/PSP3
22
RD2/PSP2
21
RC7/RX/DT
26
RC6/TX/CK
25
RC5/SDO
24
RC4/SDI/SDA
23
RA3/AN3/VREF+
5
RC0/T1OSO/T1CKI
15
MCLR/Vpp/THV
1
U1
D714D613D512D411D310D29D18D07
E6RW5RS4
VSS1
VDD2
VEE3
LCD1
LM016L
85%
FSR1
1k
IN1
2
OUT1
3
OUT2
6
OUT3
11
OUT4
14
IN2
7
IN3
10
IN4
15
EN1
1
EN2
9
VS
8
VSS
16
GND GND
U2
L293D
83%
FSR2
1k
A
B
C
D
Fig. 6 Foot pressure detection system when there is unequal
pressure in the foot.
The TABLE I shows the FSR value and motor
rotation based on it. When FSR values of Flexi force
sensor1 and Flexi force sensor2 are equal then motor
does not undergo rotation. If both FSR1 and FSR2
values are equal, the vibrating motor rotates.
TABLE 1 FSR VALUE & VIBRATING MOTOR
FSR VALUE Vibrating Motor
Equal No Rotation
Not Equal Rotates
Figure7 shows the hardware of foot detection unit.
The foot unit consists of flexi force sensors,
accelerometer, vibrating motors, MIWI transceiver
and ARM microcontroller. Figure8 shows the
hardware of handheld unit. The hand held unit
consists of ARM microcontroller, LCD display and
MIWI transceiver.
4. On Shoe Wearable Sensors For Detecting Foot Neuropathy
Proceedings of 5th
IRF International Conference, Chennai, 23rd
March. 2014, ISBN: 978-93-82702-67-2
27
Fig 7.Foot Detection System
Fig 8.Handheld Unit
CONCLUSION
This paper discussed about the detection of foot
neuropathy as early as possible in a home based
environment. Flexi force sensors are used to measure
the pressure in foot and if any abnormality is detected
in the foot pressure it is displayed on the hand held
device. The vibrating motors are rotated to stimulate
the blood flow. Thus a low-cost foot pressure and
foot movement analysis and blood flow stimulation
system, embedded within smart footwear is
developed which a patient can wear at any place to
monitor his or her foot pressure distribution to
identify and diagnose foot neuropathy as early as
possible.
REFERENCES
[1] Benoit Mariani, Mayt´e Castro Jim´enez, Franc¸ois J. G.
Vingerhoets, and Kamiar Aminian, “On-Shoe Wearable
Sensors for Gait and Turning Assessment of Patients With
Parkinson’s Disease”, in IEEE Transactions on Biomedical
Eng., 2013, vol.60, No. 1, p.p.155-158.
[2] Bor-Rong Chen, Shyamal Patel, Thomas Buckley, Ramona
Rednic, Douglas J. McClure, Ludy Shih, Daniel Tarsy, Matt
Welsh, and Paolo Bonato,“A Web-Based System for Home
Monitoring of Patients With Parkinson’s Disease Using
Wearable Sensors”, in IEEE Transactions on Bio medical
Eng., 2011, vol.58, No.3, p.p.831-836.
[3] L. Cunningham, S. Mason, C. Nugent, G. Moore, D. Finlay,
and D. Craig,“Home-Based Monitoring and Assessment of
Parkinson’s Disease”, in IEEE Transactions on Bio medical
Eng., 2011 vol. 15, No. 1, p.p.47-53.
[4] Franco Valzania Luca Palmerini, Sabato Mellone, Guido
Avanzolini, and Lorenzo Chiari, “Quantification of Motor
Impairment in Parkinson’s Disease Using an Instrumented
Timed Up and Go Test”, in IEEE Transactions on Neural
sys and Rehabiliation., 2013, vol. 21, No. 4, p.p.664-673.
[5] George Rigas, Alexandros T. Tzallas et al.,“Assessment of
Tremor Activity in the Parkinson’s Disease Using a Set of
Wearable Sensors”, in IEEE Transactions on Bio medical
Eng., 2012, vol. 16, No. 3, p.p.478-487.
[6] Nicholas Wickstrom, et al., “A New Measure of Movement
Symmetry in Early Parkinson’s Disease Patients Using
Symbolic Processing of Inertial Sensor Data”, in IEEE
Transactions on Bio medical Eng., 2011, vol. 58, No. 7,
p.p.2127-2135,2011.
[7] M.M.K. Oishi, M.J. McKeown, “Switched manual pursuit
tracking to measure motor performance in Parkinson’s
disease”, in IET Control Theory Appl., 2011, Vol. 5, Iss. 17,
p.p.1970–1977.
[8] Shyamal Patel, Konrad Lorincz, Richard Hughes, Nancy
Huggins, John Grow don, David Standaert, Metin Akay,
“Monitoring Motor Fluctuations in Patients With
Parkinson’s Disease Using Wearable Sensors”, in IEEE
Transactions on Bio medical Eng., 2009 vol. 13, No. 6,
p.p.864-873.
[9] I. Tien, S. D. Glaser, and M. J. Aminoff, “Characterization
of gait abnormalities in Parkinson’s disease using a wireless
inertial sensor system”, in IEEE Transactions on Bio
medical Eng., 2010 p.p.3353-3356.
[10] Tjitske Heida, Jeroen P. P. van Vugt, Jan A. G.
Geelen“Ambulatory Monitoring of Activities and Motor
Symptoms in Parkinson’s Disease”, in IEEE Transactions
on Bio medical Eng., 2010, vol. 57, No. 11, p.p.2778-2786.
