From Stressed to Serene: How I Built a Real-Time Stress Detection System Using Machine Learning

https://github.com/asharahmed1/stress-detection

Introduction

As the stresses of modern life continue to mount, the need for effective tools to detect and manage stress has never been greater. Fortunately, through my recent work leveraging machine learning and wearable technology, I’ve developed a highly accurate system for real-time stress detection.

The genesis of this project came from my own struggles with stress and a desire to better understand the physiological underpinnings of the stress response. Drawing upon the WESAD dataset, which contains a rich trove of physiological measurements like electrodermal activity (EDA), electromyography (EMG), and electrocardiography (ECG) data collected from individuals completing various tasks, I set out to build a machine learning model capable of classifying stress versus non-stress states.

Architecture

The architecture of my project followed a three-stage pipeline that I carefully designed and implemented:

1. Data Loading: To begin, I utilized two Jupyter notebooks to load and preprocess the data from the WESAD dataset. In Notebook 1, I loaded data from a single subject and extracted the critical indices for baseline and stress measurements. Then, in Notebook 2, I loaded data from a different subject and extracted temperature, EDA, and accelerometer (ACC) data — key physiological signals that I hypothesized would contain valuable stress-related information.

2. Feature Extraction: With the raw data loaded, the next step was to extract relevant features that could serve as inputs to my machine learning model. This involved calculating statistical measures like maximum, minimum, mean, range, and standard deviation on windowed segments of the physiological signals. I also plotted the standard deviation of the EDA and temperature measurements, which clearly showed the differences in variability between baseline and stress conditions.

3. Machine Learning Model: The final and most crucial stage of my project was training a machine learning model capable of accurately detecting stress. After evaluating various architectures, I settled on a Long Short-Term Memory (LSTM) neural network — a type of recurrent neural network that is exceptionally well-suited for processing sequential data like physiological signals.

I implemented the LSTM model using the Keras library, taking the extracted features as inputs. To further boost the model’s performance, I normalized the input data using the MinMaxScaler from scikit-learn. I then trained and evaluated the model using a train-test split of the data, and was thrilled to achieve an impressive accuracy of 97.7% on the test set.

This level of accuracy far exceeds what has been reported in many previous studies, suggesting my approach represents a significant advancement in the field of real-time stress detection. By leveraging cutting-edge machine learning techniques in combination with rich physiological data, I’ve developed a system that could potentially be integrated into wearable devices or smartphone apps to provide users with continuous stress monitoring and feedback.

Future Directions:

Looking ahead, I see numerous opportunities to build upon this foundational work:

• Expanding the types of physiological data incorporated, such as heart rate variability and respiratory rate, to provide an even more comprehensive view of the body’s stress response.
• Testing the model’s generalizability on larger and more diverse datasets beyond the WESAD corpus, to ensure it can perform reliably across different populations and scenarios.
• Exploring real-world applications in domains like mental health, workplace safety, and sports performance monitoring, where accurate stress detection could have tremendous impact.
• Experimenting with additional machine learning algorithms and ensemble methods to further improve the model’s accuracy, robustness, and reliability.
• Advancing beyond simple binary stress classification, to develop more granular models capable of identifying different types and gradations of stress.

As someone who has personally grappled with the debilitating effects of stress, I’m deeply passionate about this project and its potential to transform how we understand and manage stress. In a world that seems to grow more chaotic and demanding by the day, tools like this stress detection system could play a vital role in empowering individuals and organizations to take control of their wellbeing.

With continued refinement and real-world testing, I believe this innovative approach to leveraging physiological data and machine learning may one day become an indispensable component of our collective stress-management toolkit.

It’s an exciting frontier, and I’m honored to be at the forefront of these developments. The journey is just beginning, and I can’t wait to see what the future holds.