Smart Depression Detection: A KNN-Based Multimodal Approach for Stress Level Classification

Abstract

Mental health issues like stress and sadness are becoming more common in today\'s fast-paced, extremely demanding society. Early detection and intervention are therefore essential to stop these conditions from deteriorating. Using data gathered from the Smart_Pillow_Stress_Detection_Machine_Learning dataset, this project offers a smart mobile app that uses machine learning algorithms to forecast user stress and depression levels. Given its simplicity and efficacy in managing health-related behavioral data, the program uses the K-Nearest Neighbors (KNN) classification approach. The system uses sleep patterns, heart rate, and other vital signs recorded via a smart pillow to examine physiological and behavioral parameters in order to categorize the user\'s mental state into different degrees of stress and sadness. Supported healthier and more balanced lives, this smart prediction tool seeks to help people monitor their mental health and act timely.

Introduction

Mental health issues, especially stress and depression, have become increasingly prevalent in the 21st century due to fast-paced, digitally connected lifestyles. Despite the high global impact—depression affects over 264 million people—traditional mental health evaluations are often inconvenient and inaccessible.

Emerging smart healthcare technologies like smartwatches and smart pillows enable continuous, non-invasive monitoring of physiological and behavioral data. These devices, combined with machine learning, offer new opportunities for real-time mental health prediction. The smart pillow, equipped with sensors tracking sleep quality, heart rate, and movement, serves as a key data source for detecting stress and sadness.

This project uses the Smart_Pillow_Stress_Detection_Machine_Learning dataset and employs the K-Nearest Neighbors (KNN) algorithm, a straightforward and efficient supervised learning method, to classify users’ mental states via a mobile app. The app aims to provide timely mental health insights, raising awareness and promoting early intervention.

Additionally, AI and computer vision techniques analyzing facial expressions can aid depression detection by identifying unique behavioral patterns like reduced expressiveness or sad micro-expressions. Advances in machine learning and mobile sensing have significantly expanded access to mental health monitoring, potentially closing gaps between diagnosis and treatment through continuous, personalized care.

The literature review highlights numerous machine learning models applied to mental health prediction, with varied approaches including neural networks, SVM, and decision trees, often achieving high accuracy.

Conclusion

In this research, a stress level classifier system was implemented using the dataset and the K-Nearest Neighbours (KNN) algorithm. The system accurately classified stress at three levels—Low, Medium, and High—with a total accuracy of over 94%. With extensive preprocessing, model optimization, and assessment using metrics such as accuracy, precision, recall, and confusion matrix, the system proved high in reliability and low in misclassification. This model was incorporated into a depression detection application, in which it is used as the foundation of the prediction module. Coupled with an assessment interface, the application provides an applicable, real-time mental health monitoring tool. The method demonstrates that machine learning, particularly KNN, has the potential to contribute significantly to improved personal well-being through accessible and intelligent stress detection.

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Copyright

Copyright © 2025 Dipannita Ganai, Baishakhi Adhikary, Abhijit Banerjee. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.