AI Stress Detection System Based on Multimodal Data: Intelligent Health Monitoring Integrating Physiological and Behavioral Signals

This article introduces an open-source AI stress detection project that integrates physiological signals (such as heart rate, skin conductance response, body temperature) and behavioral data (such as activity patterns, sleep quality, mobile phone usage habits). It uses random forest and neural network technologies to predict stress levels, addressing the issues of poor real-time performance and limited accuracy in traditional subjective assessments, and providing a feasible solution for real-time health monitoring.

In fast-paced modern life, stress affects physical and mental health, and long-term accumulation can lead to problems such as anxiety and depression. Traditional stress assessment relies on questionnaires and subjective self-reports, which have shortcomings like poor real-time performance and limited accuracy. With the development of wearable devices and AI technology, automated detection based on physiological and behavioral data has become possible, opening up a new path for real-time objective health monitoring.

The project uses multimodal data (physiological + behavioral) to build a stress assessment model. Data preprocessing includes cleaning, missing value handling, outlier detection, and feature scaling; feature engineering extracts mean, variance, peak values, frequency domain features, etc., from raw time series, and may also use sliding window segmentation to facilitate training.

The project implements a dual-model architecture of random forest and neural network. Random forest has interpretability and anti-overfitting capabilities, and can evaluate feature importance; neural networks (such as MLP, LSTM, CNN) capture nonlinear relationships and temporal dependencies, and automatically learn features. The classification strategy divides stress into levels such as low/medium/high, and outputs prediction results and confidence levels.

The project supports real-time monitoring, can be integrated with wearable devices, and performs inference on the edge or cloud, sending reminders to relax when abnormalities are detected. Application scenarios include workplace health management (monitoring employee stress to prevent burnout), medical care (assisting mental health assessment), driver fatigue monitoring, student exam anxiety assessment, etc.

Current challenges include data privacy protection (requiring encryption and anonymization) and model generalization ability (adapting to different individuals/cultures). Future directions: introducing Transformer to process multimodal time-series data; using federated learning to protect privacy and improve the model; developing lightweight models to support real-time operation on mobile devices.

The AI stress detection system based on multimodal data is an important progress in intelligent health monitoring. It integrates physiological and behavioral data with AI technology to provide a real-time objective assessment solution. As the technology matures and becomes popular, personalized "stress managers" will help people actively manage their health and improve their quality of life.