Stress Sense is a health concerned research project for effective diagnosis of mental stress levels. It involves binary classification of mental stress, excluding the stress in rest condition.
-
Stress has become a major concern in modern societies.
-
It can lead to:
a) Low Productivity
b) Health Issues
c) Increased Cardiac Disease risk
-
This project aims to provide a novel approach in estimating stress using photo-plethysmo-graph (PPG) Signals.
-
Why PPG?
a) Non-Invasive Method
b) Low Cost
c) Easy to measure
- Implement EMD on PPG signal.
- Feature extraction from IMFs.
- Develop a ML model to estimate stress level
- Evaluate the performance of classification model
Step 1: Collection of Required Data
- Utilized publicly available MAUS dataset. (Link - [1])
Step 2: Data Preprocessing
- Data segregation into low and high sections
- Data merging of all participants.
- Applied Standard scaling
Step 3: Emperical Mode Decomposition for Feature Extraction
- Decomposing ppg signals into Intrinsic Mode Functions(IMFs)
- Extracting statistical features namely, mean, minimum, maximum and skewness from the different number of IMFs (keeping in mind max imfs for all sets of data)
Step 4: Training and Classification
- Kept test size as 30% of the labeled dataset
- Implemented Random Forest Classifier
- Implemented Support Vector Machine(SVM) for classification.
- Performed Regularization
Empowering Stress Estimation with PPG signals->
- Significance of Mental Stress Assessment: The evaluation of mental stress plays a pivotal role in the realm of human-computer interaction.
- Innovative Signal Decomposition: Employed the Empirical Mode Decomposition (EMD) technique to decompose PPG signals into Intrinsic Mode Functions (IMFs) and extract relevant statistical features.
- Machine Learning for Stress Classification: Trained a Support Vector Machines (SVM) classification model on the extracted features to categorize PPG signals into low stress and high stress states.
- Achieving Accuracy and Precision: Our approach yielded an impressive accuracy rate of 82% and precision rate of 85% in estimating stress levels using PPG signals.
- Effective Information Capture: Leveraging EMD and statistical features proved to be an effective method for capturing pertinent information related to stress within PPG signals.
- Try to explore new methods
- Deploying the model
- Develop an app for direct diagnosis of mental stress level
- https://ieee-dataport.org/open-access/maus
- D. Jaiswal, A. Chowdhury, D. Chatterjee, and R. Gavas, “Unobtrusive smart-watch based approach for assessing mental workload,” in Proc. IEEE Region 10th Symp., 2019, pp. 304–309
- F. Schaule, J. O. Johanssen, B. Bruegge, and V. Loftness, “Employing consumer wearables to detect office workers’ cognitive load for interruption management,” Proc. ACM Interactive, Mobile, Wearable Ubiquitous Technol., vol. 2, no. 1, pp. 1–20, 2018
- D. Ekiz, Y. S. Can, and C. Ersoy, “Long short-term network based unobtrusive perceived workload monitoring with consumer grade smartwatches in the wild,” IEEE Trans. Affect. Comput., p. 1, 2019, doi: 10.1109/TAFFC.2021.3110211
- https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9664394&tag=1
- https://www.mql5.com/en/forum/emd-imf
- https://www.researchgate.net/Exploring_EEG_Empirical_Mode_Decomposition
- python
- Symmetric Projection Attractor Reconstruction (SPAR)
- Cross Wavelet Transform (XWT)
- Chirp Z Transform (CZT)
- Heart Rate Variability (HRV)
- Emperical Mode Decomposition (EMD)
Krishna Dubey (ML and Reasearch), Pankaj Kumar Giri (Reasearch)