Abstract:
Remote work provides flexibility, but it also magnifies concerns about employee well-being in terms of emotional instability, stress, and mental health. The traditional way of monitoring
mental health and performance evaluation is flawed for a remote working environment since it lacks both real-time ability and integration of emotional and physiological data. This
acknowledges the need for an Intelligent System that will help in emotion detection, mental
health monitoring, and personalized intervention to enhance the productivity and well-being of remote employees.
In this proposed solution, state-of-the-art machine learning algorithms, including
Convolutional Neural Networks (CNN) for emotion classification in real time, and Random
Forest Classifiers for predicting stress levels through HRV data, were employed. Natural
Language Processing (NLP) is used to detect psychological patterns in terms of sentiment. It uses a combination of heart rate data, webcam-based facial recognition for mental health
monitoring. Developing a web-based application serves as the primary interface for the
monitoring and emotion detection of a user's mental state for employees working remotely in real-time, and includes the visualization of emotion history, stress trends analysis, and
automated personalized music recommendations & therapy suggestions. A lightweight mobile companion app interfaces with to provide real-time heart rate data.
The prototype achieved an overall accuracy of 92%. It was evaluated using a dataset of over
20,000 facial images, heart rate data, and stress prediction based on these inputs. The final
output included music recommendations and therapy suggestions. Real-time evaluations
demonstrated the system’s responsiveness and usability across various platforms, highlighting its potential to significantly improve remote employee well-being and enhance organizational performance.
