Introduction
Mental health is an essential aspect of an individual’s overall well-being, affecting
daily functioning, social relationships, and physical aspects of life. In the modern
era, anxiety and depression are on the rise as a result of increasing life pressures,
which calls for technological solutions to monitor individuals’ moods and provide
early psychological support when needed.
Recent studies indicate that more than 280 million people worldwide suffer from
depression, according to the World Health Organization (WHO), making it one of
the most common mental disorders. However, many of those affected do not have
access to appropriate healthcare in a timely manner, either due to lack of
awareness or limited resources. Therefore, finding innovative solutions based on
smart technology can greatly contribute to improving the early detection of
unstable psychological conditions and providing appropriate responses.
Accordingly, an Arduino-based Integrated Mood and Depression Assessment
System has been developed, which is a smart system that uses biosensing
technologies and real-time data analysis to detect physiological and behavioral
changes that may indicate a deterioration in mood. This system collects data such
as heart rate, physical activity level, and voice changes, and then analyzes them
using artificial intelligence to provide an accurate assessment of the user's
condition. It can also send alerts to individuals themselves or to healthcare
providers when indicators of psychological danger are detected .
This system is an important step towards promoting mental health using modern
technology, as it provides a convenient and quick way for people to monitor their
moods without the need for complex medical examinations, which contributes to
reducing the incidence of depression and improving the quality of life .
Objectives
This system is designed to achieve the following objectives :
1) Provide a smart tool to monitor mood and depression using accurate
biosensors .
2) Analyze physiological and behavioral data to detect changes that may
indicate severe mood swings .
3) Alert users or caregivers when signs of depression or worrisome
changes in psychological state are detected .
4) Encourage prevention and early intervention by providing data-based
recommendations on how to improve psychological state .
5) Enhancing the use of artificial intelligence and the Internet of Things
(IoT) in the field of mental health to help people in their daily lives .
Technical and Social Challenges
1) Technical Challenges :
• Reducing the error rate in data analysis to ensure accurate diagnosis
of psychological conditions .
• Improving energy consumption so that the device can operate for long
periods without the need for constant recharging.
• Integrating the device with smart health applications to ensure easy
access to data and providing suggestions to improve mood.
2) Social Challenges:
• Convincing users to adopt technology to monitor their mental health
and overcome privacy concerns .
• Ensuring security and protecting user data from any hacking or
misuse .
• Raising awareness of the importance of monitoring mood regularly as
part of general health care .
Expected Results
• This system represents a quantum leap in the field of mental health,
and is expected to contribute to:
• Improving early diagnosis of depression and mood disorders, which
helps individuals take timely therapeutic measures .
• Reducing the need for visits to psychiatrists by providing a tool that
helps individuals understand their psychological condition
themselves .
• Supporting healthcare providers with accurate information about
patients’ conditions, which facilitates them in making better treatment
decisions .
• Improving quality of life by empowering individuals to take control of
their mental health and helping them improve their daily habits based
on the data provided by the device.
BY: M.S.C Huda Wasfi Hasson