Analysing Stress Levels of Engineering Students in the First Semester

Abstract

Abstract: This invention relates to an innovative system designed for real-time stress monitoring among first-semester engineering students. The system addresses the increasing need for mental health support in academic environments, where students often face significant stress due to academic pressure, social adjustments, and the transition from high school to a more challenging engineering curriculum. The core of the system involves wearable devices, such as smartwatches or wristbands, that continuously collect physiological data from the students. These wearable devices monitor key biomarkers, including heart rate variability (HRV) and galvanic skin response (GSR), which are direct indicators of stress. By tracking these physiological signals, the system can detect both acute and chronic stress levels in real-time. The wearables are designed to be non-intrusive, ensuring that students can wear them throughout the day without disruption to their activities. The collected data is transmitted to a cloud-based platform via a mobile application that students have access to on their smartphones. The cloud-based system processes the data using advanced machine learning algorithms, which are specifically trained to recognize stress patterns unique to first-semester engineering students. These algorithms take into account not only physiological data but also external factors such as academic workload, peer interactions, and social activities. This integration of psychological, physiological, and environmental data ensures a more accurate and comprehensive analysis of each student's stress levels. Based on the analysis, the system provides personalized recommendations aimed at helping students manage their stress. These recommendations can include stress-relief exercises like deep breathing, mindfulness techniques, or even suggestions for time management strategies. For students experiencing prolonged stress, the system can alert academic counselors, enabling them to intervene before the stress leads to more severe mental health or academic issues. This real-time stress monitoring system empowers students to take control of their mental well-being while providing institutions with the tools to support their students more effectively. Through continuous monitoring and timely intervention, the system helps students manage stress, improving their academic performance and overall well-being.

Specification

Description:Title: Analysing Stress Levels of Engineering Students in the First Semester
Field of the Invention: The present invention relates to the field of mental health assessment and education technology. More particularly, it involves a method and system for analyzing stress levels among first-semester engineering students to improve their academic performance and well-being. The present invention offers an advantage in providing real-time stress assessment using biofeedback and machine learning.
Background of the Invention: Research indicates that first-semester engineering students often face significant stress due to the sudden transition from high school to a more rigorous academic environment. Factors like workload, peer pressure, and adjusting to a new educational setup can lead to high-stress levels. Despite awareness of this issue, existing tools to assess student stress levels lack accuracy and real-time capabilities, resulting in interventions that are often too late.
Several prior arts exist in the domain of stress detection and academic interventions. One such prior art is titled "System and Method for Monitoring Stress Using Physiological Signals," (Patent Application Number US20130281802A1) which uses physiological signals such as heart rate and skin conductance to determine stress levels. Another prior art, "Wearable Device for Monitoring Stress Levels," (Patent Application Number US20170020099A1) proposes a wearable solution that tracks stress using biomarkers but lacks integration with academic settings.
Yet another prior art titled "Mental Health Monitoring System for Students," (Patent Application Number WO2020172514A1) focuses on monitoring stress but is limited to periodic assessments, which might not provide real-time data necessary for timely interventions.
From the prior art descriptions, it is understood that existing systems are either focused solely on physiological measurements or lack academic integration. None of the prior arts sufficiently address the real-time monitoring of stress levels specific to engineering students during their first semester, a critical period for academic and emotional adjustment.



Object of the Present Invention: The primary objective of the present invention is to provide a system that can monitor and analyze stress levels in engineering students during their first semester in real-time.
• Another object is to integrate physiological and psychological parameters using biofeedback mechanisms and machine learning algorithms to assess stress more comprehensively.
• A further objective is to provide academic institutions with actionable insights based on stress level analysis, allowing for timely intervention to support students.
• The system will also aim to provide personalized recommendations to students for managing stress, based on historical and real-time data.
















Summary of the Invention: The invention provides a method and system for real-time stress level analysis among engineering students in their first semester. The system employs a combination of physiological sensors and machine learning algorithms to continuously monitor stress indicators such as heart rate, skin conductance, and self-reported stress levels. The data is processed in real-time and analyzed using AI algorithms to determine a student's stress level. Based on this analysis, personalized recommendations and academic counseling are offered.
According to one aspect of the invention, the system integrates wearable devices with a mobile app that students can use to monitor their stress levels. Another aspect involves real-time data transmission to a centralized system where machine learning algorithms analyze the stress data and provide timely interventions.


















Brief Description of the Drawings:
Figure 1 illustrates a block diagram of the stress monitoring system, showing the integration of sensors, data processing units, and output modules.
Figure 2 illustrates a flowchart depicting the stress analysis process, from data collection to the generation of personalized recommendations.
Figure 3 illustrates Use Case Diagram


















Detailed Description of the Invention:
The present invention introduces a comprehensive and real-time stress monitoring system designed specifically for first-semester engineering students. The system leverages advancements in wearable technology, cloud computing, and machine learning to continuously monitor and analyze stress levels based on physiological and environmental factors. The goal is to provide students with personalized stress management interventions and enable academic counselors to offer timely support based on real-time data insights.
1. Wearable Physiological Sensors:
At the core of the system are wearable physiological sensors, such as heart rate monitors and galvanic skin response (GSR) sensors. These devices measure critical stress-related biomarkers that reflect the student's physiological state in real-time. The heart rate monitor detects changes in heart rate variability (HRV), a proven indicator of stress. Similarly, the GSR sensor measures the skin's electrical conductance, which increases with sweat gland activity-a physiological response linked to emotional stress.
These sensors are embedded in user-friendly wearables, such as wristbands or smartwatches, to ensure continuous and unobtrusive monitoring. These devices are capable of detecting subtle physiological changes during a student's daily routine, including study sessions, examinations, or peer interactions. The wearables are equipped with Bluetooth or Wi-Fi technology to transmit data to a mobile application seamlessly.
2. Mobile Application for Data Collection and User Interaction:
The wearable sensors are paired with a dedicated mobile application, which serves as the main interface for the students and the system's data collection hub. The app continuously receives physiological data from the sensors, processes it, and prepares it for transmission to a cloud-based system. Additionally, the mobile application allows students to interact with the system, providing them with insights into their stress levels, notifications, and personalized recommendations.
The mobile app is also equipped with a self-reporting feature, where students can manually input their perceived stress levels or any other subjective experiences they wish to log, such as mood or fatigue. This feedback loop is essential to improving the accuracy of the stress analysis algorithms, as it combines both objective (physiological) and subjective (self-reported) data. The self-reporting feature also helps to create a more holistic understanding of the student's stress patterns.
3. Cloud-Based System and Data Analysis:
Once the physiological data is collected via the mobile app, it is transmitted to a cloud-based system where the real-time processing and analysis take place. This cloud system serves as a central repository and processing center for all incoming data. Using the scalable storage and processing capabilities of cloud computing, the system is capable of handling data from thousands of students simultaneously.
In the cloud, machine learning algorithms specifically trained to recognize stress patterns among first-semester engineering students analyze the data. These algorithms have been designed using extensive training datasets that include physiological and psychological stress markers commonly observed in students undergoing academic stress, such as high workloads, examinations, and social adjustment challenges. By leveraging the power of machine learning, the system can distinguish between stress caused by academic workload and other stressors, providing a tailored analysis for each student.
4. Analysis of External Variables:
In addition to physiological data, the system also considers external variables that may affect the student's stress levels. These variables include academic workload (such as assignment deadlines or exam schedules), social interactions (peer pressure, group work), and even environmental factors (sleep, exercise, and daily routines). The system pulls these variables from integrated academic scheduling platforms and user-reported data within the mobile app. By integrating external factors into the analysis, the system offers a comprehensive view of the student's overall stress profile.
For example, if the student's physiological data indicates elevated heart rate and skin conductance during a period of heavy workload (as noted by their academic schedule), the system attributes the stress to academic causes. In contrast, if stress indicators rise during social activities, peer pressure or social anxiety might be identified as the cause. This granularity allows for a more tailored response and personalized recommendations.


5. Machine Learning-Based Stress Pattern Recognition:
The heart of the invention lies in its machine learning model, which continuously learns and adapts to recognize stress patterns specific to each student. This model is built upon a neural network that processes physiological data, self-reported information, and external factors to identify when stress levels cross critical thresholds.
The machine learning model is updated regularly with new data, improving its accuracy over time. It can recognize not just momentary spikes in stress, but also long-term trends that may indicate chronic stress or burnout. The system uses predictive analytics to forecast when a student might be at risk of stress-related issues, such as decreased academic performance or mental health challenges, based on their historical data and current physiological indicators.
6. Personalized Recommendations:
Once stress is detected and analyzed, the system generates personalized recommendations aimed at helping students manage their stress levels effectively. The recommendations are tailored based on the severity of stress and the specific causes identified by the system. For instance:
• If the system detects that a student's heart rate has increased beyond a set threshold during a study session, it might suggest short stress-relief exercises like deep breathing or a brief walk.
• If chronic stress is detected over several days, the system may recommend academic counseling, provide tips on time management, or suggest mindfulness practices. Also if needed we will escalate the issue to Clinical Psychiatrist.
• In cases where the system predicts an imminent risk of burnout (based on prolonged high stress levels), it may prompt the academic counselor to intervene and provide further guidance or refer the case to Clinical Psychiatrist.
These recommendations are delivered via the mobile app and can be scheduled to appear during moments when stress is likely to peak, such as before exams or assignment deadlines. The recommendations are backed by evidence-based stress management strategies, including Cognitive-Behavioral Techniques, NLP, mindfulness practices, and time management tips.


7. Longitudinal Data and Academic Counselor Interventions:
Over time, the system accumulates a wealth of longitudinal data on each student's stress levels. This data provides a detailed history of the student's physiological and emotional responses to various stressors. Academic counselors can access this data through a secure dashboard to monitor the stress trends of individual students or cohorts.
The longitudinal data allows academic counselors to identify patterns of chronic stress or academic burnout. They can use this information to provide targeted interventions, such as recommending workload adjustments, suggesting counseling services, or offering academic support programs. The system can also generate periodic reports summarizing the student's stress trends, which can be discussed during academic counseling sessions. In case beyond, the client will be referred to Clinical Psychiatrist.
In this embodiment, the system acts as a bridge between students and academic staff, ensuring that no student is left unsupported when their stress levels become overwhelming. By providing both real-time data and long-term trends, the system enables proactive interventions rather than reactive measures.
8. Feedback Loop for Improved Accuracy:
A unique aspect of the invention is its feedback loop. After receiving a stress management recommendation, students can provide feedback on its effectiveness through the mobile application. This feedback is then used to refine the machine learning model, making future recommendations more accurate and personalized. For example, if a student finds that mindfulness exercises do not help them, the system may adjust its recommendations to focus on other techniques like time management or academic support.
The feedback loop ensures that the system evolves with the student's changing needs, improving its ability to provide effective stress management over time. Eclectic therapy may also be used based on the requirements of the Client.








Claims:
I/We Claim
1. A real-time stress monitoring system for engineering students, comprising:
o Wearable sensors configured to detect physiological stress indicators;
o A mobile application for data collection and student feedback;
o A cloud-based machine learning system for real-time stress analysis;
o Personalized stress management recommendations based on analyzed data.
2. As claimed in claim 1, wherein the physiological stress indicators include heart rate variability and skin conductance.












Abstract:
This invention relates to an innovative system designed for real-time stress monitoring among first-semester engineering students. The system addresses the increasing need for mental health support in academic environments, where students often face significant stress due to academic pressure, social adjustments, and the transition from high school to a more challenging engineering curriculum.
The core of the system involves wearable devices, such as smartwatches or wristbands, that continuously collect physiological data from the students. These wearable devices monitor key biomarkers, including heart rate variability (HRV) and galvanic skin response (GSR), which are direct indicators of stress. By tracking these physiological signals, the system can detect both acute and chronic stress levels in real-time. The wearables are designed to be non-intrusive, ensuring that students can wear them throughout the day without disruption to their activities.
The collected data is transmitted to a cloud-based platform via a mobile application that students have access to on their smartphones. The cloud-based system processes the data using advanced machine learning algorithms, which are specifically trained to recognize stress patterns unique to first-semester engineering students. These algorithms take into account not only physiological data but also external factors such as academic workload, peer interactions, and social activities. This integration of psychological, physiological, and environmental data ensures a more accurate and comprehensive analysis of each student's stress levels.
Based on the analysis, the system provides personalized recommendations aimed at helping students manage their stress. These recommendations can include stress-relief exercises like deep breathing, mindfulness techniques, or even suggestions for time management strategies. For students experiencing prolonged stress, the system can alert academic counselors, enabling them to intervene before the stress leads to more severe mental health or academic issues.
This real-time stress monitoring system empowers students to take control of their mental well-being while providing institutions with the tools to support their students more effectively. Through continuous monitoring and timely intervention, the system helps students manage stress, improving their academic performance and overall well-being.



ANNEXURE -I
Questionnaire for Assessing Stress Levels in First-Semester Engineering Students
Section 1: Demographic Information
1. Age:
2. Gender:
3. Course/Department:
4. Residential Status:
o Living on campus
o Living off-campus
o Commuting from home
5. Do you have any prior experience in engineering or technical subjects before joining this course?
o Yes
o No
Section 2: Academic Stress
1. How often do you feel overwhelmed by your academic workload?
o Never
o Rarely
o Sometimes
o Often
o Always
2. How confident are you in your ability to keep up with the course materials and assignments?
o Very confident
o Somewhat confident
o Neutral
o Somewhat unconfident
o Very unconfident
3. How frequently do you find yourself worrying about upcoming exams or deadlines?
o Never
o Rarely
o Sometimes
o Often
o Always
4. On a scale of 1 to 5, how much pressure do you feel to perform well academically (with 1 being low and 5 being very high)?
o 1
o 2
o 3
o 4
o 5
Section 3: Psychological and Emotional Stress
1. How often do you feel anxious or stressed due to academic demands?
o Never
o Rarely
o Sometimes
o Often
o Always
2. Do you experience difficulty in concentrating on your studies due to stress or anxiety?
o Never
o Rarely
o Sometimes
o Often
o Always
3. How often do you have trouble sleeping due to academic or personal stress?
o Never
o Rarely
o Sometimes
o Often
o Always
4. Do you experience physical symptoms (e.g., headaches, fatigue, upset stomach) as a result of stress?
o Never
o Rarely
o Sometimes
o Often
o Always
Section 4: Social and Peer-Related Stress
1. How comfortable do you feel socializing and interacting with your peers?
o Very comfortable
o Somewhat comfortable
o Neutral
o Somewhat uncomfortable
o Very uncomfortable
2. How often do you feel pressured by your peers to succeed or perform better?
o Never
o Rarely
o Sometimes
o Often
o Always
3. On a scale of 1 to 5, how supported do you feel by your classmates and friends?
o 1 (Not supported at all)
o 2
o 3
o 4
o 5 (Very supported)
Section 5: Coping Strategies and Support
1. When you feel stressed, how often do you engage in physical activities (e.g., exercise, sports) to reduce stress?
o Never
o Rarely
o Sometimes
o Often
o Always
2. How often do you practice relaxation techniques (e.g., deep breathing, meditation, yoga)?
o Never
o Rarely
o Sometimes
o Often
o Always
3. Do you seek support from academic Counselors or mentors when you are stressed?
o Yes
o No
4. How often do you find time for hobbies or activities that help you unwind from stress?
o Never
o Rarely
o Sometimes
o Often
o Always
5. How effective are the stress-relief techniques you use?
o Very effective
o Somewhat effective
o Neutral
o Somewhat ineffective
o Very ineffective
















Coping Strategies Based on Stress Monitoring System Analysis
Based on the system's analysis of stress levels, personalized coping strategies are recommended. Here are some general coping strategies that can be tailored to the individual based on real-time data:
1. Mindfulness and Meditation:
o Practice mindfulness meditation for 10 minutes a day to focus on the present and reduce anxiety.
o Use guided meditation apps to practice relaxation techniques during stressful moments.
2. Time Management Techniques:
o Create a daily schedule that breaks down academic tasks into manageable segments.
o Prioritize assignments and focus on completing one task at a time to avoid feeling overwhelmed.
3. Physical Activity:
o Engage in regular physical exercise such as walking, jogging, or yoga for at least 30 minutes a day.
o Stretch or take short breaks during study sessions to improve focus and relieve stress.
4. Social Support:
o Talk to friends, family, or classmates to share concerns and build a support network. If you feel no one is at your support, meet a Counsellor.
o Participate in group study sessions to reduce academic stress and promote peer support.
5. Breathing Exercises:
o Use deep breathing techniques to quickly reduce stress during exams or high-pressure situations.
o Inhale deeply for 4 seconds, hold for 4 seconds, and exhale for 4 seconds, repeating until you feel more relaxed.
6. Seeking Professional Help:
o Contact an academic counselor or mental health professional if stress becomes overwhelming.
o Participate in workshops on stress management and mental wellness offered by the institution.
7. Self-Reflection and Journaling:
o Keep a stress journal to track stressful situations and identify patterns that contribute to high stress levels.
o Reflect on positive achievements each day to reinforce a sense of progress and reduce negative self-talk.
8. Healthy Sleep Habits:
o Establish a consistent sleep routine by going to bed at the same time each night.
o Avoid late-night studying or screen time before bed to improve the quality of sleep.












, Claims:I/We Claim
1. A real-time stress monitoring system for engineering students, comprising:
o Wearable sensors configured to detect physiological stress indicators;
o A mobile application for data collection and student feedback;
o A cloud-based machine learning system for real-time stress analysis;
o Personalized stress management recommendations based on analyzed data.
2. As claimed in claim 1, wherein the physiological stress indicators include heart rate variability and skin conductance.

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