Hybrid deep learning model for wearable sensor-based stress recognition for Internet of Medical Things (IoMT) system

There are numerous, interrelated, and multi-dimensional aspects that influence a person's mental health, one of them being stress. Smart wearable technology having physiological and motion sensors has paved the way for real-time data collection to deliver cutting-edge information about the stress of individuals. It is now possible to build an Internet of Medical Things (IoMT) system that can recognise the user's stress, revealing the elements that cause stress. However, there are significant gaps in the existing system for stress recognition. To begin with, stress recognition is primarily studied for a specific set of people, such as occupational stress among office working persons or hospital staff during emergency duties, and so forth. Second, most past work on stress recognition has focused on extracting handcrafted traits, which necessitates human interaction and skill. To overcome above mentioned challenges, this work proposes a novel IoMT framework for continuous stress recognition for mental well-being. This paper presents a hybrid deep learning (DL) approach for automatically retrieving features and classification into various stress states for the IoMT system to address these difficulties. The proposed expanded system gathers data from wearable physiological sensors and feeds it into a convolutional neural network-long short-term memory (CNN-LSTM), a hybrid DL classifier. The suggested approach has been tested on the wearable stress and affect detection (WESAD) dataset. It has a reported accuracy of 90.45%, which is higher than previously reported accuracies from existing machine learning (ML) and DL approaches. This paper proposes a framework for continuous stress recognition that incorporates physiological sensors and a hybrid deep learning approach for automatic feature extraction and classifying the input signal to various affective states. The proposed hybrid CNN-LSTM deep learning model achieves an accuracy of 90.45% on WESAD (benchmark dataset for stress recognition from wearable sensors). This framework overcomes existing challenges that focus on specific groups of people and also handcrafted features. image