AI Stress Detection System Based on Multimodal Data: From Physiological Signals to Mental Health

This article introduces an open-source AI stress detection project that uses multimodal data (physiological signals such as heart rate, skin conductance response + behavioral data such as activity patterns, sleep data) combined with random forest and neural network models to predict stress levels. It supports real-time monitoring and can be applied in scenarios like wearable devices, workplace health management, and clinical auxiliary diagnosis.

Traditional stress assessment relies on questionnaires and subjective reports, which have lag and insufficient accuracy. With the popularization of wearable devices and the development of AI technology, real-time stress monitoring based on physiological signals and behavioral data has become possible, addressing the pain points of traditional methods.

This project is an open-source machine learning and deep learning project focused on multimodal data fusion to predict stress levels. Multimodal data includes physiological signals (heart rate, skin conductance response) and behavioral data (activity patterns, sleep data). By fusing heterogeneous data, it provides a more comprehensive understanding of the user's stress state.

The project uses two models: 1. Random Forest (an ensemble learning method that builds multiple decision trees to reduce overfitting, handles high-dimensional features, and evaluates the importance of indicators); 2. Neural Network (automatically learns complex nonlinear relationships, captures temporal patterns and hidden features of physiological signals, and identifies subtle changes in stress).

Raw physiological data contains noise and missing values. The project implements a complete preprocessing process: signal filtering, outlier detection, and missing value imputation; feature engineering extracts statistical features (mean, variance, peak value) and frequency domain features; feature scaling (standardization/normalization) ensures consistent feature scales and avoids numerical dominance in training.

1. Wearable device integration: Combine with smart watches/bands to collect data in real time and remind users to adjust when stress levels rise; 2. Workplace health management: Monitor employees' stress, identify high-risk groups, and assist HR in adjusting work arrangements; 3. Clinical auxiliary diagnosis: Serve as an auxiliary tool for traditional psychological assessment, providing objective data to assist in the diagnosis of anxiety, depression, etc.

Technical highlights: Clear code structure, including data processing, model training and evaluation processes, supports reproduction and transfer learning, and provides classification result visualization. Summary: This project demonstrates a practical stress monitoring system combining machine learning and physiological signal processing; Outlook: Future advancements in sensors and accumulation of high-quality datasets will improve the system's accuracy and universality, safeguarding public health.