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International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 7 Issue 4, July-August 2023 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD59673 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 375
Design and Development of Mental Health Monitoring System
using Multiple Sensors Integrated with Arduino Uno
Vijay Kumar, Poonam Kumari
University Centre of Instrumentation and Microelectronics, Panjab University, Chandigarh, India
ABSTRACT
There has been a revolution in the use of mobile health devices for
monitoring physical health. There is more recent interest in whether
these devices can also be used for monitoring symptoms of mental
illness. The paper discusses how mobile based system can be
employed for detecting physiological signs of stress. Stress causes
deviations in biometrics such as EDA, heart rate etc. measurement of
these biometrics using a handheld device can allow patients to self-
monitor and clinicians to detect the early warning signs. The system
developed is employing Arduino UNO to interface sensors like
temperature sensor (LM35), pulse oximeter (MAX30102), pulse
sensor, GSR sensor and LCD.
KEYWORDS: Mental Health, Skin Resistance, SpO2, Heart Rate,
Temperature
How to cite this paper: Vijay Kumar |
Poonam Kumari "Design and
Development of Mental Health
Monitoring System using Multiple
Sensors Integrated with Arduino Uno"
Published in
International
Journal of Trend in
Scientific Research
and Development
(ijtsrd), ISSN:
2456-6470,
Volume-7 | Issue-4,
August 2023, pp.375-380, URL:
www.ijtsrd.com/papers/ijtsrd59673.pdf
Copyright © 2023 by author (s) and
International Journal of Trend in
Scientific Research and Development
Journal. This is an
Open Access article
distributed under the
terms of the Creative Commons
Attribution License (CC BY 4.0)
(http://creativecommons.org/licenses/by/4.0)
I. INTRODUCTION
Mental health is a vital aspect of overall well-being,
and its significance has gained increasing recognition
in recent years. However, mental health issues
continue to pose significant challenges worldwide.
[3,4] The advent of technology has opened up new
opportunities to address these challenges and provide
effective support to individuals in managing their
mental health. One such technological advancement
is the development of mental health monitoring
systems.
The introduction of mental health monitoring systems
marks a significant step forward in promoting mental
well-being and addressing the complexities associated
with mental health issues. These systems leverage the
power of sensors, data analysis algorithms, and user-
friendly interfaces to monitor and analyse various
physiological and behavioural parameters that are
indicative of an individual's mental health status. By
continuously collecting and analysing this data,
mental health monitoring systems offer personalized
insights, early detection of potential issues, and
access to timely interventions. [13,14]
This paper aims to explore the concept of mental
health monitoring systems and integration of mental
health monitoring systems into existing healthcare
frameworks.
By examining the potential of mental health
monitoring systems, this paper focused on harnessing
technology for the betterment of mental well-being,
this research endeavours to pave the way for more
effective and accessible support systems for
individuals facing mental health challenge.
II. Literature Review:
The following papers were studied and analysed in
details and hence summarized in the below table:
IJTSRD59673
International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD59673 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 376
Table 1: Paper studied
Reference
Main considered
devices
Description
Gravenhorst, F. et
al.
smartphones
Overview of how mobile phones can support the
treatment of mental disorders
Nicholas, J. et al. smartphones
A systematic review of android and iOS applications for
bipolar disorder
Pantelopoulos, A.
and Bourbakis, N.G
wearable sensors
Survey’s research and developments on wearable
biosensor systems for health monitoring.
Donker, T. et al. smartphones /tablets
A systematic review of research evidence supporting the
efficacy of mental health apps for mobile devices.
Bayndr, L. smartphones
A survey about using smartphones to detect human
behaviour including health related activities like physical
exercise and sleeping.
Mohr, D.C. et al.
smartphones and
other wearable
sensors
A review of personal sensing research related to mental
health and a framework to convert raw data into
knowledge
Stephens, J. and
Allen, J.
smartphones
Systematic review of smartphone applications and text
messaging in promoting weight reduction and physical
activity
Guntuku, S.C. et al. social media
A review of recent studies that analyse social media to
detect depression and mental illness.
Wang, J. et al. smartphones
A review of smartphone interventions for long-term
chronic condition management including mental health
problems.
Huguet, A. et al. smartphones
Systematic review of self-help apps for people with
depression and evaluate those that offer cognitive
behavioural therapy or behavioural activation.
Alotaiby, T.N. et al. electroencephalogram
A survey of seizure detection and prediction challenges
and algorithms.
Mosenia A. et al. wearable sensors
A survey of applications and architecture of wearable
medical sensors systems.
Study of papers revealed that currently all the
techniques used for monitoring devices are either
using single sensor with the IOT or are using two to
three sensors mostly heart rate and temperature sensor
or GSR sensor which limits the parameter to conduct
the diagnosis. These mental health monitoring
systems are only compatible with certain sensors,
limiting the types of data that can be collected and
analysed.
III. Materials and Methods:
The system we are developing is able to transmit the
monitored parameters at far places. It employs
multiple sensors like HR, Temperature, skin
conductance and SpO2 that presents comprehensive
data for person’s health.
Main objective of this system is to integrate various
sensors to collect data, transmit and being
observed/analysed by professionals as and when
needed.
A. Block Diagram:
Fig.1: Block diagram
B. Hardware Requirements:
Arduino Uno
Temperature Sensor (LM35)
International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD59673 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 377
Pulse Oximeter (MAX30102)
Pulse Sensor
Galvanic Skin Resistance Sensor
Wi-Fi module (ESP8266)
Liquid Crystal Display (LCD)
C. Hardware Implementation:
Connecting the LM35 Temperature Sensor to an
Arduino
Hooking up the LM35 to an Arduino is super simple.
You only need to connect three pins: two for power
and one for reading the sensor value.
The sensor can be powered from 5V. The positive
voltage connects to ‘+Vs’ and ground connects to
‘GND’. The middle pin ‘Vout’ is the analog signal
output from the sensor and connects to the A0 analog
input of an Arduino.
Fig.2: Connection of LM35 with Arduino Uno
Wiring up a MAX30102 Module to an Arduino:
Start by connecting the VCC pin to the power supply,
3V-5V is fine. Use the same voltage that your
microcontroller logic is based off of. For most
Arduinos, that is
5V. For 3.3V logic devices, use 3.3V. Now connect
GND to common ground. Connect the SCL pin to the
I2C clock pin and the SDA pin to the I2C data pin on
your Arduino. Note that each Arduino Board has
different I2C pins which should be connected
accordingly. On the Arduino boards with the R3
layout, the SDA (data line) and SCL (clock line) are
on the pin headers close to the AREF pin. They are
also known as A5 (SCL) and A4 (SDA).
The following illustration shows the wiring:
Fig.3: Connection of MAX30102 with Arduino
Uno
Wiring a Pulse Sensor to an Arduino:
Connecting the Pulse Sensor to an Arduino is a
breeze. You only need to connect three wires: two for
power and one for reading the sensor value.
The module can be supplied with either 3.3V or 5V.
Positive voltage is connected to ‘+,’ while ground is
connected to ‘-.’ The third ‘S’ wire is the analog
signal output from the sensor, which will be
connected to the Arduino’s A0 analog input.
The following is the wiring diagram for the Pulse
Sensor experiments:
Fig.4: Connection of pulse sensor with Arduino
Uno
GSRsensorinterfacing:
Following diagram depicts GSR sensor interfacing
with Arduino board. Following are the connections
between pins of both GSR sensor and arduino board.
Vcc (GSR sensor side) - 5V (Arduino side)
GND – GND
SIG - A0
Fig.5: Connection of GSR sensor with Arduino
Uno
Wiring a 16×2 Character LCD to an Arduino:
HD44780-based LCDs are designed so that we can
communicate with them using only four data pins (in
4-bit mode) rather than eight (in 8-bit mode). This
helps us save 4 I/O pins! So, to interface the LCD in
4-bit mode, only six pins are required: RS, EN, D7,
D6, D5, and D4. Connect the LCD’s four data pins
(D4-D7) to digital pins 5 to 2 on the Arduino, the EN
pin to digital pin 11, and the RS pin to digital pin 12.
International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD59673 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 378
The wiring is shown below.
Fig.6: Connection of LCD with Arduino Uno
D. Circuit Design:
In the circuit the signal starts from the sensors which
continuously monitors the parameters like heart rate,
spO2, temperature, skin resistance and send signal to
Arduino through the analog pins of the sensors to
Arduino’s analog I/O pins which are read byArduino.
Then the monitored data is sent to display through
digital Input/output pins of the Arduino. The LCD
display’s the monitored parameters. Meanwhile all
the parameters recorded are also sent to web page
through ESP8266 WIFI module so that the mental
health professional also check the parameters.
Fig.7: Complete circuit Diagram
E. Flow Chart of Project:
The given flow chart shows the flow of data and
signals from sensors till the data displayed on LCD
and web page.
Fig.8: Flow chart of project
IV. Result:
The project is tested by activating the Arduino and all
the sensors of the system that is MAX30102, pulse
sensor, LM35, GSR sensors. All the components of
the project are working in synchronization with each
other. The data is recorded by the Arduino in real
time. Output is sent to LCD and laptop display and
visible in the real time.
Working model after the sensors are put on the finger
and the readings over the display showing different
parameters.
Fig.9a: working model temperature readings
International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD59673 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 379
Fig.9b: working model RED and IR readings
Fig.9c: working model RED and IR readings
The same parameters are displayed on serial monitor
of the IDE software:
Fig.10a: working model RED and IR readings
on serial monitor
Fig.10b: working model temperature readings
on serial monitor
V. Conclusion:
In conclusion, the development of mental health
monitoring systems represents a valuable and timely
approach to address mental health challenges. By
combining technological innovation, personalized
interventions, and comprehensive data analysis, these
systems have the potential to revolutionize mental
health care, improving the well-being and quality of
life for individuals facing mental health issues. It is
imperative that ongoing research, development, and
collaboration continue to advance the field, ensuring
that mental health monitoring systems become an
integral part of comprehensive mental health care in
the future.
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@ IJTSRD | Unique Paper ID – IJTSRD59673 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 380
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Design and Development of Mental Health Monitoring System using Multiple Sensors Integrated with Arduino Uno

  • 1. International Journal of Trend in Scientific Research and Development (IJTSRD) Volume 7 Issue 4, July-August 2023 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470 @ IJTSRD | Unique Paper ID – IJTSRD59673 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 375 Design and Development of Mental Health Monitoring System using Multiple Sensors Integrated with Arduino Uno Vijay Kumar, Poonam Kumari University Centre of Instrumentation and Microelectronics, Panjab University, Chandigarh, India ABSTRACT There has been a revolution in the use of mobile health devices for monitoring physical health. There is more recent interest in whether these devices can also be used for monitoring symptoms of mental illness. The paper discusses how mobile based system can be employed for detecting physiological signs of stress. Stress causes deviations in biometrics such as EDA, heart rate etc. measurement of these biometrics using a handheld device can allow patients to self- monitor and clinicians to detect the early warning signs. The system developed is employing Arduino UNO to interface sensors like temperature sensor (LM35), pulse oximeter (MAX30102), pulse sensor, GSR sensor and LCD. KEYWORDS: Mental Health, Skin Resistance, SpO2, Heart Rate, Temperature How to cite this paper: Vijay Kumar | Poonam Kumari "Design and Development of Mental Health Monitoring System using Multiple Sensors Integrated with Arduino Uno" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-4, August 2023, pp.375-380, URL: www.ijtsrd.com/papers/ijtsrd59673.pdf Copyright © 2023 by author (s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0) I. INTRODUCTION Mental health is a vital aspect of overall well-being, and its significance has gained increasing recognition in recent years. However, mental health issues continue to pose significant challenges worldwide. [3,4] The advent of technology has opened up new opportunities to address these challenges and provide effective support to individuals in managing their mental health. One such technological advancement is the development of mental health monitoring systems. The introduction of mental health monitoring systems marks a significant step forward in promoting mental well-being and addressing the complexities associated with mental health issues. These systems leverage the power of sensors, data analysis algorithms, and user- friendly interfaces to monitor and analyse various physiological and behavioural parameters that are indicative of an individual's mental health status. By continuously collecting and analysing this data, mental health monitoring systems offer personalized insights, early detection of potential issues, and access to timely interventions. [13,14] This paper aims to explore the concept of mental health monitoring systems and integration of mental health monitoring systems into existing healthcare frameworks. By examining the potential of mental health monitoring systems, this paper focused on harnessing technology for the betterment of mental well-being, this research endeavours to pave the way for more effective and accessible support systems for individuals facing mental health challenge. II. Literature Review: The following papers were studied and analysed in details and hence summarized in the below table: IJTSRD59673
  • 2. International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD59673 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 376 Table 1: Paper studied Reference Main considered devices Description Gravenhorst, F. et al. smartphones Overview of how mobile phones can support the treatment of mental disorders Nicholas, J. et al. smartphones A systematic review of android and iOS applications for bipolar disorder Pantelopoulos, A. and Bourbakis, N.G wearable sensors Survey’s research and developments on wearable biosensor systems for health monitoring. Donker, T. et al. smartphones /tablets A systematic review of research evidence supporting the efficacy of mental health apps for mobile devices. Bayndr, L. smartphones A survey about using smartphones to detect human behaviour including health related activities like physical exercise and sleeping. Mohr, D.C. et al. smartphones and other wearable sensors A review of personal sensing research related to mental health and a framework to convert raw data into knowledge Stephens, J. and Allen, J. smartphones Systematic review of smartphone applications and text messaging in promoting weight reduction and physical activity Guntuku, S.C. et al. social media A review of recent studies that analyse social media to detect depression and mental illness. Wang, J. et al. smartphones A review of smartphone interventions for long-term chronic condition management including mental health problems. Huguet, A. et al. smartphones Systematic review of self-help apps for people with depression and evaluate those that offer cognitive behavioural therapy or behavioural activation. Alotaiby, T.N. et al. electroencephalogram A survey of seizure detection and prediction challenges and algorithms. Mosenia A. et al. wearable sensors A survey of applications and architecture of wearable medical sensors systems. Study of papers revealed that currently all the techniques used for monitoring devices are either using single sensor with the IOT or are using two to three sensors mostly heart rate and temperature sensor or GSR sensor which limits the parameter to conduct the diagnosis. These mental health monitoring systems are only compatible with certain sensors, limiting the types of data that can be collected and analysed. III. Materials and Methods: The system we are developing is able to transmit the monitored parameters at far places. It employs multiple sensors like HR, Temperature, skin conductance and SpO2 that presents comprehensive data for person’s health. Main objective of this system is to integrate various sensors to collect data, transmit and being observed/analysed by professionals as and when needed. A. Block Diagram: Fig.1: Block diagram B. Hardware Requirements: Arduino Uno Temperature Sensor (LM35)
  • 3. International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD59673 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 377 Pulse Oximeter (MAX30102) Pulse Sensor Galvanic Skin Resistance Sensor Wi-Fi module (ESP8266) Liquid Crystal Display (LCD) C. Hardware Implementation: Connecting the LM35 Temperature Sensor to an Arduino Hooking up the LM35 to an Arduino is super simple. You only need to connect three pins: two for power and one for reading the sensor value. The sensor can be powered from 5V. The positive voltage connects to ‘+Vs’ and ground connects to ‘GND’. The middle pin ‘Vout’ is the analog signal output from the sensor and connects to the A0 analog input of an Arduino. Fig.2: Connection of LM35 with Arduino Uno Wiring up a MAX30102 Module to an Arduino: Start by connecting the VCC pin to the power supply, 3V-5V is fine. Use the same voltage that your microcontroller logic is based off of. For most Arduinos, that is 5V. For 3.3V logic devices, use 3.3V. Now connect GND to common ground. Connect the SCL pin to the I2C clock pin and the SDA pin to the I2C data pin on your Arduino. Note that each Arduino Board has different I2C pins which should be connected accordingly. On the Arduino boards with the R3 layout, the SDA (data line) and SCL (clock line) are on the pin headers close to the AREF pin. They are also known as A5 (SCL) and A4 (SDA). The following illustration shows the wiring: Fig.3: Connection of MAX30102 with Arduino Uno Wiring a Pulse Sensor to an Arduino: Connecting the Pulse Sensor to an Arduino is a breeze. You only need to connect three wires: two for power and one for reading the sensor value. The module can be supplied with either 3.3V or 5V. Positive voltage is connected to ‘+,’ while ground is connected to ‘-.’ The third ‘S’ wire is the analog signal output from the sensor, which will be connected to the Arduino’s A0 analog input. The following is the wiring diagram for the Pulse Sensor experiments: Fig.4: Connection of pulse sensor with Arduino Uno GSRsensorinterfacing: Following diagram depicts GSR sensor interfacing with Arduino board. Following are the connections between pins of both GSR sensor and arduino board. Vcc (GSR sensor side) - 5V (Arduino side) GND – GND SIG - A0 Fig.5: Connection of GSR sensor with Arduino Uno Wiring a 16×2 Character LCD to an Arduino: HD44780-based LCDs are designed so that we can communicate with them using only four data pins (in 4-bit mode) rather than eight (in 8-bit mode). This helps us save 4 I/O pins! So, to interface the LCD in 4-bit mode, only six pins are required: RS, EN, D7, D6, D5, and D4. Connect the LCD’s four data pins (D4-D7) to digital pins 5 to 2 on the Arduino, the EN pin to digital pin 11, and the RS pin to digital pin 12.
  • 4. International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD59673 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 378 The wiring is shown below. Fig.6: Connection of LCD with Arduino Uno D. Circuit Design: In the circuit the signal starts from the sensors which continuously monitors the parameters like heart rate, spO2, temperature, skin resistance and send signal to Arduino through the analog pins of the sensors to Arduino’s analog I/O pins which are read byArduino. Then the monitored data is sent to display through digital Input/output pins of the Arduino. The LCD display’s the monitored parameters. Meanwhile all the parameters recorded are also sent to web page through ESP8266 WIFI module so that the mental health professional also check the parameters. Fig.7: Complete circuit Diagram E. Flow Chart of Project: The given flow chart shows the flow of data and signals from sensors till the data displayed on LCD and web page. Fig.8: Flow chart of project IV. Result: The project is tested by activating the Arduino and all the sensors of the system that is MAX30102, pulse sensor, LM35, GSR sensors. All the components of the project are working in synchronization with each other. The data is recorded by the Arduino in real time. Output is sent to LCD and laptop display and visible in the real time. Working model after the sensors are put on the finger and the readings over the display showing different parameters. Fig.9a: working model temperature readings
  • 5. International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD59673 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 379 Fig.9b: working model RED and IR readings Fig.9c: working model RED and IR readings The same parameters are displayed on serial monitor of the IDE software: Fig.10a: working model RED and IR readings on serial monitor Fig.10b: working model temperature readings on serial monitor V. Conclusion: In conclusion, the development of mental health monitoring systems represents a valuable and timely approach to address mental health challenges. By combining technological innovation, personalized interventions, and comprehensive data analysis, these systems have the potential to revolutionize mental health care, improving the well-being and quality of life for individuals facing mental health issues. It is imperative that ongoing research, development, and collaboration continue to advance the field, ensuring that mental health monitoring systems become an integral part of comprehensive mental health care in the future. REFERENCES: [1] H. A. Whiteford, L. Degenhardt, J. Rehm, A. J. Baxter, A. J. Ferrari, H. E. Erskine, F. J. Charlson, R. E. Norman, A. D. Flaxman, N. Johns, et al., Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010, Lancet 382 (9904) (2013) 1575–1586. [2] G. V. Polanczyk, G. A. Salum, L. S. Sugaya, A. Caye, L. A. Rohde, Annual research review: Ameta-analysis of the worldwide prevalence of mental disorders in children and adolescents, J. Child Psychol. Psychiatry 56 (3) (2015) 345– 365. [3] J. M. Twenge, Time period and birth cohort differences in depressive symptoms in the us, 1982–2013, Social Indic. Res. 121 (2) (2015) 437–454. [4] M. Olfson, B.G. Druss, S. C. Marcus, Trends in mental health care among children and adolescents, New England J. Med. 372 (21) (2015) 2029–2038. [5] R. C. Kessler, R. G. Frank, The impact of psychiatric disorders on work loss days, Psychol. Med. 27 (4) (1997) 861873. [6] M. W. DeVries, B. Wilkerson, Stress, work and mental health: a global perspective, Acta Neuropsychiatr. 15 (1) (2003) 44–53. [7] K. Demyttenaere, R. Bruffaerts, J. Posada- Villa, I. Gasquet, V. Kovess, J. P. Lepine, M. C. Angermeyer, S. Bernert, G. de Girolamo, P. Morosini, G. Polidori, T. Kikkawa, N. Kawakami, Y. Ono, T. Takeshima, H. Uda, E. G. Karam, J. A. Fayyad, A. N. Karam, Z. N. Mneimneh, M. E. Medina-Mora, G. Borges, C. Lara, R. de Graaf, J. Ormel, O. Gureje, Y. Shen, Y. Huang, M. Zhang, J. Alonso, J. M.
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