The Portable Tools of the Elderly Alzheimer Patients
Yulastri, Era Madona, Anggara Nasution and M. Irmansyah
Jurusan Teknik Elektro Politeknik Negeri Padang Jl. Limau Manih Padang, 25164, Indonesia
Keywords: Alzheimer's Patient, Microcontroller, GPS, MPU6050, SMS.
Abstract: In this study we propose a portable tool to monitor and monitor patients at risk of Alzheimer's. The aim of
this research is to design and make a useful tool to determine the position and condition of Alzheimer's
sufferers. The research stages consisted of designing hardware, software and testing the whole tool. To find
out the patient's condition, an accelerometer sensor was used which would read the values of the x, y, and z
axes to determine whether the condition was okay or falling. This tool is also equipped with a panic button if
the patient does not know his position and forgets to go home, an SMS notification will be sent. The results
of the test, the device can send an SMS notification of the patient's position when the supervisor sends a sms,
"GPS ON and the navigation module can determine the exact location by moving the reading position of the
GPS module to the test point with an average of 1.99 meters. Overall the tool can function properly.
1 INTRODUCTION
Alzheimer's disease is a progressive degenerative
disease of the brain that commonly affects older
people. This disease is characterized by confusion,
disorientation, memory failure, speech problems, and
dementia. The risk of developing Alzheimer's
increases with age (Neugroschl, J., & Wang, S. 2011),
(McKhann et al.,2011). There are approximately 47
million people who suffer from Alzheimer's disease
in the world (Ricci, 2019) and as many as 27 million
of them are in Asia (Aggarwal, N. T et al., 2012).
Indonesia based on the results of the 2014 National
Economic Survey, the number of elderly people in
Indonesia reached 20.24 million people or about
8.03% of the entire population of Indonesia. This data
shows an increase when compared to the results of the
2010 Population Census, namely 18.1 million people
or 7.6% of the total population. Alzheimer's disease
is most commonly seen in older people > 65 years of
age, but it can also affect people around 40 years of
age (Holtzman, 2020). Alzhaimer's disease can make
the elderly get lost when walking out and forget to
walk home (Caspi E, 2014). Lack of information and
time to report to the police as well as inadequate
CCTV face identification tools because their use is
only applied to certain areas is a problem in
monitoring Alzheimer's patients. Based on data from
the Radio Suara Surabaya Research Team, from
January 2017 to July 2019, Radio Suara Surabaya has
received 618 listener reports about missing people
due to dementia or senility (Yulastri, 2021).
Due to this, an action is needed in the form of
surveillance for someone who experiences symptoms
of Alzheimer's (Galvin J. E, 2017).
The development of information
technology has
given birth to technological
innovations in the health
sector, including research
(Khodkari et al., 2019).
Mobile application-based
surveillance of patients
with disease-at-risk has been
developed for diabetes
(Aulia et al., 2018), cardiac
(Islam et al., 2018) and
stroke (Amritphale et al.,
2017) patients. In
addition, microcontroller-based
patient monitoring
with notification has also been
carried out for
vertigo patients (Yulastri et al., 2020),
premature
infants (M. Irmansyah et al., 2019) and the
risk of
prolonged sitting (E.Madona et al., 2018). In
this
study we made a small, low-cost, portable device
to
monitor and monitor patients with Alzheimer's
symptoms. Several related studies have been carried
out before a study conducted by (Siregar et al., 2018)
used Arduino, gyroscope and accelerometer sensors
to measure the falling motion of the elderly, the
results showed the system could detect and
distinguish between conscious and accidental falls.
Using the MPU6050 sensor with a 3-axis
accelerometer and a 3-axis gyroscope (Jefiza et al.,
2017) conducted a study to detect elderly activities
with the backpropagation method used for motion
recognition. The results showed an accuracy rate of
948
Yulastri, ., Madona, E., Nasution, A. and Irmansyah, M.
The Portable Tools of the Elderly Alzheimer Patients.
DOI: 10.5220/0010957100003260
In Proceedings of the 4th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2021), pages 948-955
ISBN: 978-989-758-615-6; ISSN: 2975-8246
Copyright
c
 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

0.1818 with an ROC of 98.12%. A similar study was
also conducted by (Aphairaj et al., 2019) using the
line application as LINEBOT if the elderly were
detected falling. The results showed the system could
send alarms with high effectiveness.
The development of the previous research are
(1)
Notifications are cheaper because they use SMS
compared to the internet network, (2) Monitoring can
be done in two directions where the family can find
out the patient's position by sending an SMS to the
device used by the patient.
The approach to making this tool uses a wearable
sensor based (M. Mubashir et al., 2013). Wearable
sensor based, used for device cost efficiency,
installation and arrangement of the design is also not
complicated. Therefore, the device is relatively easy
to operate (A. Hakim et al., 2017). With this tool the
supervisor can find out the position of the patient by
sending an SMS to the device attached to the patient
with a certain format. This tool is also equipped with
a panic button if the patient does not know its position
and forgets to go home. In addition, this tool also uses
an accelerometer sensor which is a protection for
Alzheimer's patients when the majority of patients are
elderly when they go out and fall, the sensor will
detect the tilt of the patient, the majority of whom are
elderly people, fall left, right, backward and forward
and send notifications. SMS to supervisor for the
coordinate of the patient's position. The aim of this
research is to design and make a useful tool to
determine the position and condition of Alzheimer's
sufferers. It is hoped that this tool can be used as a
monitoring and tracking tool, especially for
Alzheimer's patients to stay healthy and protected in
their activities.
2 METHOD
The method used in this research is making
prototype tools starting from literature study, system
design, hardware design, software design, hardware
testing, software and analysis of test results. This tool
for monitoring the position of Alzheimer's patients is
used for elderly patients who are placed on their
waist, when Alzheimer's patients leave the house for
a long time and forget to go home, the supervisor
wants to know their location, so orders are carried out
by the user or supervisor with a specific command
format sent to SIM800L. Then the message on the
SIM800L will be processed by Arduino nano. The
working principle of the tool can be seen in Figure 1.
Location data sent by GPS U-Blox Neo coordinates
the location of the patient's whereabouts.
This data
Figure 1: System Block Diagram.
will be processed by Arduino nano which will later
be sent to SIM800L. SIM800L will forward
the
contents of the message in the form of the location
coordinates of the whereabouts to the supervisor's
cellphone. This coordinate point is opened in the
Google Maps application where from the message
there is a web url address. In the Google Maps
application, the patient's location can be seen in the
form of a digital map. The push button functions as a
panic button if the patient does not know his position
and forgets to go home, Arduino will process it, and
GPS U-Blox Neo will send location coordinate data
to SIM800L and then forwarded by SIM800L to the
patient supervisor's cellphone. The accelerometer /
tilt sensor functions to detect the slope of the patient,
the majority of whom are elderly people who fall,
whether they fall left, right, backward and forward.
Arduino will process it then it will be forwarded to
GPS U-Blox Neo and will send location coordinate
data to SIM800L and then forwarded by SIM800L to
the patient monitoring cellphone.
2.1 Hardware Design
This circuit consists of an Arduino nano functioning
as a microcontroller, a GSM SIM800L
module, a
GPS module, a push button, a tilt angle
detector
using the MPU6050 accelerometer. The
battery is
connected to the 5V and GND pins, as seen
in Figure
2. The MPU6050 sensor has 2 (two) pins
that will
be connected to the Arduino pin, namely pin
A5
(SCL), pin A4 (SDA) on the microcontroller
detecting the tilt of the patient's position which is
placed on protoype bag when experiencing a change
in position which is divided into several conditions.
To determine the patient's condition on the
The Portable Tools of the Elderly Alzheimer Patients
949

accelerometer sensor can be seen in table 1. The
battery as a mobile voltage source for this system is
connected to the + 5V and GND microcontroller and
+ 4V to the SIM800L GSM module. To get a voltage
of + 4VDC, a DC-DC converter is needed. The RXD
pin on the SIM is connected to pin 7 on the Arduino
and the TXD pin is connected to pin 7 of the Arduino.
The series of GPS modules is connected to the 3.3V
pin on the Arudino as a voltage input. The GPS is
connected to the GND pin on the Arduino for
grounding. The GPS module has 2 (two) pins that will
be connected to the Arduino pin, namely pin 0 (TXD)
panic button is connected to the 5V pin on Arduino as
a voltage input. Meanwhile, the output is connected
to pin 2 such as in figure 3(a). The device is made to
be placed on the patient's belt. The design of the tool
box can be seen in Figure 3(b).
Table 1: Accelerometer Sensor Value for fall detection.
Fall Direction Patient's Condition Information
x y z
Normal 0
o
26
o
- 90
o
21
o
- 90
o
The patient is fine
Fall to the left
-65
o
- -90
o
-25
o
- 0
o
90
o
The SMS is sent in the form of a patient's
location link :
www.google.com/map/place
Fall to the right
65
o
- 90
o
25
o
- 0
o
90
o
The SMS is sent in the form of a patient's
location link :
www.google.com/map/place
Fall forward
70
o
- 90
o
90
o
20
o
- 0
o
The SMS is sent in the form of a patient's
location link :
www.google.com/map/place
Fall back
-70
o
- -90
o
90
o
-20
o
- 0
o
The SMS is sent in the form of a patient's
location link :
www.google.com/map/place
Figure 2: Flowchart of Alzheimer's patient monitoring tool.
iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science
950

(a) (b)
Figure 3: Designing Tool Prototype.
2.2 Software Design
The flowchart for monitoring Alzheimer's patients
with SMS notifications can be seen in Figure 2. The
process starts with the initialization of I/O.
Furthermore, there are three stages of checking, first
the incoming SMS is in the SMS format "ON", the
second is the panic button with a value of "1" and the
Accelerometer sensor reading. The device will read
the patient's location and send an SMS notification of
the patient's location to the supervisor's cellphone.
3 RESULTS AND DISCUSSION
Furthermore, testing the tool aims to determine the
advantages and disadvantages of the system that
has
been made. The test is carried out in two stages.
First,
testing system performance, secondly testing
tool
notifications. This bag prototype-shaped device
is
then attached to the patient, as seen in Figure 4.
Figure 4: Installation of monitoring devices for Alzheimer's
patients on the body.

3.1 System Performance Testing
This test is done to determine the performance of the
tool, starting with testing the tilt sensor on the
accelerometer. The data taken is based on the slope of
the angle 0
o
, 20
o
, 25
o
70
o
, and 90
o
to the left, right,
front and back then the sensor data is displayed on the
serial monitor, so that the results are in accordance
with table 1.
Figure 5: Testing of Alzheimer's patient surveillance tools.
Based on table 1, when the patient's condition
is
normal, the accelerometer sensor is tilted x = 0
o
, y
=
26
o
- 90
o
, z = 21
o
- 90
o
. When the patient falls to
the
left, the patient is in a tilt position x = -65
o
- -90
o
,
y = -25
o
- 0
o
, z = 90
o
. Furthermore, when the patient
falls
to the right, the patient is in a tilt position x = 65
o
- 90
o
, y = 25
o
- 0
o
, z = 90
o
. When the patient falls
forward,
the patient is in a tilt position x = 70
o
- 90
o
,
y = 90
o
, z= 20
o
- 0
o
. Meanwhile, when the patient
falls
backwards x = -70
o
- -90
o
, y = 90
o
, z = -20
o
-
0
o
. Changes in the angle value will affect the sensor
value
of the MPU6050 sensor. The greater the
The Portable Tools of the Elderly Alzheimer Patients
951

Table 2: Detection test falls on the system.
Category
Number of
experiment
Notification Fall
Accuracy
%
Detection
%
Yes No
Face down 15 14 1 90 Yes No
Recumbent 15 14 1 90 90 10
Total 30 28 2

Figure 6: (a) The supervisor sends the message “GPS ON”, (b) SIM800L Measurement Point.
change in angle, the greater the sensor value of the
MPU6050
sensor. So when the sensor value of the
MPU6050
sensor shows the data value that has been
set when it
falls left, right, forward and backward,
SIM800L will
send an SMS to the number
08526359xxxx. The SMS
that contains the link for
the longitude and latitude
coordinates only when the
GPS module gets a signal.
If when the MPU6050
sensor shows the Alzheimer's
patient data has fallen
and the GPS module does not
get a signal, then the
SMS sent will only contain
“Patient location” which
does not have a link for the
longitude and latitude
coordinates. Furthermore,
testing the detection of
tools for falls and prone
incidents is carried out. The
test results can be seen in
table 2. Based on the
experiments in table 2, some
falling activities such
as falling on your back and falling on your stomach
can be detected by the system
as falling activities with
an accuracy rate of 90%.
3.2 Testing Tool Notifications
This test is conducted to determine whether the
device can send SMS notifications and determine the
position of the patient. The first test The supervisor
will send a message "GPS ON" to the patient's device
to test the SIM800L to work properly so that it can
communicate with the microcontroller as a link to the
GSM network. When the GPS module gets a signal,
a message is sent containing "Patient location
www.google.com. / map / place ”and when the GPS
module does not get a signal, the message sent is only
the message“ Patient location ”which does not have a
link for the longitude and latitude coordinates. When
sending SMS from SIM800L to the supervisor, the
voltage is 5.04V on the GND and RX pins, while
when receiving SMS the voltage is 1.38V on the
GND and TX pins. Measurement points and SMS
notifications can be seen in Figure 6.
Furthermore, testing of tool notifications is
carried out when the panic button is pressed. When
the push button is in high condition, the SIM800L
will send an sms to the supervisor, the output voltage
is 4.8V, then the GPS module gets a signal, the
message is sent "Location of the patient is
www.google.com/map/place". When receiving a
signal, the output voltage on the GPS module at the
GND and TX pins is 4.8 V, while at the time of
sending the signal, the voltage at the GND and RX
pins is 2.5V. The measurement point and SMS
notification when the panic button is pressed can be
seen in Figure 7. We hope you find the information in
this template useful in the preparation of your
submission.
Figure 7: (a) SMS notification when panic button is pressed,
(b) GPS module measurement point.
To determine how accurate is the reading of the
coordinates of the location captured by the GPS
satellites using the Ublox Neo-7M module by
iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science
952

Table 3: The difference between the shift from the GPS reading point to the test point.
No  Test Location  Goo
g
le Ma
p
Modul GPS Error
(meter)
Latitude1 Lon
g
itude1 Latitude2 Lon
g
itude2
1 location 1 -0,930057 100,424882 -0,930035 100,424848 2,79812
2 location 2 -0,940817 100,382332 -0,940811 100,382089 1,67962
3 location 3 -0,928732 100,350293 -0,928711 100,350288 1,49266
Avera
g
e 1,9901
Table 4: The results of the GPS module testing observations.
Input data Which are expected Observation and testing
Data from module (latitude
& longitude)
The GPS module can pinpoint the patient's location
The GPS module can detect the latitude and
longitude coordinates of the test well, with an average difference in the shift in the module reading points of around 1.9901
meters.
calculating the difference in the shift of the coordinate
reading point (latitude and longitude) with the
coordinate value obtained from the Google Maps
application. The test was carried out at three different
locations in the city of Padang. The coordinates of the
test position can be seen in Figure 8.
Figure 8: GPS test position.
In table 3, there are latitude1 and longitude1
values, which are taken from test points that use the
Google Maps application on a smartphone. The
latitude2 and longitude2 values are obtained from the
data from testing the U-blox Neo-7M GPS module.
Calculation of the distance between two points on a
curved surface (spherical Earth theory) by utilizing
latitude and longitude values can use the Haversine
Formula method (F. Farid and Y. Yunus, 2017). This
method is an equation for the use of navigation in
order to calculate the distance between coordinates in
the Desimanl Degree (DD °) geographic projection
system. So that the value of the difference between
the shift in points read by the GPS module will be
obtained.
Furthermore, the observation and module testing
are carried out on the patient's position as shown in
Table 4
The GPS module testing is conducted to find out
how accurate the Neo-7M GPS module is in
determining the coordinates of the location because the
GPS module's reading accuracy value greatly
affects
the process of tracking the accident location to
find the
location of the victim. The GPS module in
this study
can read the coordinates of the location
well.
4 CONCLUSION
(1). While sensor accelerometer MPU6050 in slope
range –y = -25
o
- 0
o
the conditions of patients fall to
the left. In the range y = 25
o
– 0
o
patients fall to the
right, the range z = 20
o
– 0
o
patients fall to in front
and the range z = -20
o
– 0
o
patients fall to the back.
The position of rotation point at X axis which have
slope kemiringan x
y
= 65
o
– 90
o
, -x
y
= - 65
o
– 90
o
, x
z
= 70
o
– 90
o
dan -x
z
= -70
o
– -90
o
. (2). GPS U-BLOX
NEO Module has difficulty to reach the satellite
signal to determine the longitude and latitude position
while inside the room and can get the longitude and
latitude position easily outside the room. (3) Once the
GPS U-BLOX NEO Module get the signal then the
GSM SIM800L module sent a SMS “Lokasi Pasien
www.google.com/map/place” to 08526359xxxx. If
the patient supervisor sent the SMS “GPS ON” to
08238381xxxx and push button is pushed or high
condition and the patient fall to left, right, in front or
to the back with the position shift of GPS Module
reading to the testing point average is 1.99 metre.
The Portable Tools of the Elderly Alzheimer Patients
953

REFERENCES
Neugroschl, J., & Wang, S. (2011). Alzheimer's disease:
diagnosis and treatment across the spectrum of disease
severity. The Mount Sinai journal of medicine, New
York, 78(4), 596–612. https://doi.org/10.1002/
msj.20279
McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman,
B. T., Jack, C. R., Jr, Kawas, C. H., Klunk, W. E.,
Koroshetz, W. J., Manly, J. J., Mayeux, R., Mohs, R.
C., Morris, J. C., Rossor, M. N., Scheltens, P., Carrillo,
M. C., Thies, B., Weintraub, S., & Phelps, C. H. (2011).
The diagnosis of dementia due to Alzheimer's disease:
recommendations from the National Institute on Aging-
Alzheimer's Association workgroups on diagnostic
guidelines for Alzheimer's disease. Alzheimer's &
dementia : the journal of the Alzheimer's Association,
7(3), 263–269. https://doi.org/10.1016/j.jalz.
2011.03.005
G. (2019). Social Aspects of Dementia Prevention from a
Worldwide to National Perspective: A Review on the
International Situation and the Example of Italy.
Behavioural Neurology , 2019. https://doi.org/10.1155/
2019/8720904
Aggarwal, N. T., Tripathi, M., Dodge, H. H., Alladi, S., &
Anstey, K. J. (2012). Trends in Alzheimer's disease and
dementia in the asian-pacific region. International
journal of Alzheimer's disease, 2012, 171327.
https://doi.org/10.1155/2012/171327
2020 Alzheimer's disease facts and figures. (2020).
Alzheimer's & dementia : the journal of the Alzheimer's
Association, 10.1002/alz.12068. Advance online
publication. https://doi.org/10.1002/alz.12068
Caspi E. (2014). Wayfinding difficulties among elders with
dementia in an assisted living residence. Dementia
(London, England), 13(4), 429–450.
https://doi.org/10.1177/1471301214535134
https://www.suarasurabaya.net/kelanakota/2019/Sejak-
2017-SS-Mencatat-Ada-293-Kasus-Lansia-Hilang-
Karena-Demensia/
Galvin J. E. (2017). Prevention of Alzheimer's Disease:
Lessons Learned and Applied. Journal of the American
Geriatrics Society, 65(10), 2128–2133. https://doi.org/
10.1111/jgs.14997
Khodkari, H., Maghrebi, S. G., Asosheh, A., &
Hosseinzadeh, M. (2019). Smart Healthcare and
Quality of Service Challenges. 9th International
Symposium on Telecommunication: With Emphasis on
Information and Communication Technology, IST
2018, 253–257. https://doi.org/10.1109/ISTEL.
2018.8661125
Aulia, A., Tanzil, F., Wairooy, I. K., Gunawan, L. K.,
Cunwinata, A., & Albert. (2018). A development of
android-based mobile application for getting ideal
weight. Telkomnika (Telecommunication Computing
Electronics and Control), 16(3), 1289–1294.
https://doi.org/10.12928/TELKOMNIKA.v16i3.8342
Islam, M., Sadhukhan, R. K., Haque, M. M., Rahman, N.,
Alam, S. S., Chowdhury, M. S. R., Abdullah-Al-Kaiser,
M., & Shahnaz, C. (2018). Android based heart rate
monitoring and automatic notification system. 5th IEEE
Region 10 Humanitarian Technology Conference 2017,
R10-HTC 2017, 2018-January, 436–439.
https://doi.org/10.1109/R10-HTC.2017.8288993
Amritphale, A., Amritphale, N., & Dubey, D. (2017).
Smartphone applications providing information about
stroke: Are we missing stroke risk computation
preventive applications? Journal of Stroke, 19(1), 117.
https://doi.org/10.5853/jos.2016.01004r
Yulastri, E. Madona, M. Irmansyah, and A. Nasution.
(2020) Alat Deteksi Jatuh Berbiaya Murah Dengan
Tracking Position Untuk Pasien Vertigo dan Sinkop. J.
RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4,
no. 6, pp. 9–11, 2020, doi: 10.29207/resti.v4i6.2608.
M. Irmansyah, E. Madona, and A. Nasution (2019). Design
and application of portable heart rate and weight
measuring tool for premature baby with microcontroller
base. Int. J. GEOMATE, vol. 17, no. 61, pp. 195–201,
2019, doi: 10.21660/2019.61.ICEE12.
E. Madona, M. Irmansyah, and A. Nasution (2018). Sistem
Informasi Untuk Posisi Dan Lama Duduk Dengan
Smartphone Android Berbasis Mikrokontroler.
Elektron J. Ilm., vol. 10, no. 2, pp. 1–5, 2018, doi:
10.30630/eji.10.2.75.
M. Mubashir, L. Shao, and L. Seed (2013). A survey on
fall detection: Principles and approaches.
Neurocomputing, vol. 100, pp. 144–152, 2013, doi:
10.1016/j.neucom.2011.09.037.
A. Hakim, M. S. Huq, S. Shanta, and B. S. K. K. Ibrahim
(2017). Smartphone Based Data Mining for Fall
Detection: Analysis and Design. Procedia Comput.
Sci., vol. 105, no. December 2016, pp. 46–51, 2017,
doi: 10.1016/j.procs.2017.01.188.
F. Farid and Y. Yunus (2017). Analisa Algoritma Haversine
Formula Untuk Pencarian Lokasi Terdekat Rumah
Sakit Dan Puskesmas Provinsi Gorontalo. Ilk. J. Ilm.,
vol. 9, no. 3, pp. 353–355, 2017, doi: 10.33096/
ilkom.v9i3.178.353-355.
Siregar, B., Andayani, U., Bahri, R. P., Seniman, & Fahmi,
F. (2018). Real-time monitoring system for elderly
people in detecting falling movement using
accelerometer and gyroscope. Journal of Physics:
Conference Series , 978(1). https://doi.org/10.1088/
1742-6596/978/1/012110
Jefiza, A., Pramunanto, E., Boedinoegroho, H., &
Purnomo, M. H. (2017). Fall detection based on
accelerometer and gyroscope using back propagation.
International Conference on Electrical Engineering,
Computer Science and Informatics (EECSI), 2017-
Decem(September), 19–21. https://doi.org/10.1109/
EECSI.2017.8239149
Aphairaj, D., Kitsonti, M., & Thanapornsawan, T. (2019).
Fall detection system with 3-axis accelerometer.
Journal of Physics: Conference Series, 1380(1).
https://doi.org/10.1088/1742-6596/1380/1/012060.
Holtzman, D. M., Mandelkow, E., & Selkoe, D. J. (2012).
Alzheimer disease in 2020. Cold Spring Harbor
perspectives in medicine, 2(11), a011585.
Ricci, W. A., Lu, Z., Ji, L., Marand, A. P., Ethridge, C. L.,
Murphy, N. G., ... & Zhang, X. (2019). Widespread
iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science
954

long-range cis-regulatory elements in the maize
genome. Nature plants, 5(12), 1237-1249.
Yulastri, Y., Madona, E., Nasution, A., & Irmansyah, M.
(2021). The Elderly Alzheimer Patient’s Portable Tools
for Position Detection with SMS Notifications.
Indonesian Journal of Electronics, Electromedical
Engineering, and Medical Informatics, 3(4), 156-163.
The Portable Tools of the Elderly Alzheimer Patients
955