Exposure to continuous stress can negatively impact a person's mental health. Stress monitoring plays a significant role to detect the stress level of an individual. The main goal of this work is to develop a quantum augmented deep learning model to support knowledge workers in the self management of their stress levels in the office. Quantum computing is a technological leap that is expected to provide us with the fastest computational power than today's classical processors. In this paper, a parameterized quantum circuit is augmented in the traditional Long Short Term memory (LSTM) model to enhance its learning capability and improve the predictions. The effectiveness of the proposed method is evaluated on various sensor data obtained from SWELL-KW [1] dataset. The time series dataset contains knowledge worker's computer interaction, facial expression, body postures and heart rate (variability) and skin conductance data recorded in varied working conditions. With improved learning ability, the proposed model has shown promising prediction ability on data obtained from all the modalities. For dataset in hand, the model with heart rate variability data resulted in maximum F1-score of 87.67%.
Exposure to continuous stress can negatively impact a person's mental health. Stress monitoring plays a significant role to detect the stress level of an individual. The main goal of this work is to develop a quantum augmented deep learning model to support knowledge workers in the self management of their stress levels in the office. Quantum computing is a technological leap that is expected to provide us with the fastest computational power than today's classical processors. In this paper, a parameterized quantum circuit is augmented in the traditional Long Short Term memory (LSTM) model to enhance its learning capability and improve the predictions. The effectiveness of the proposed method is evaluated on various sensor data obtained from SWELL-KW [1] dataset. The time series dataset contains knowledge worker's computer interaction, facial expression, body postures and heart rate (variability) and skin conductance data recorded in varied working conditions. With improved learning ability, the proposed model has shown promising prediction ability on data obtained from all the modalities. For dataset in hand, the model with heart rate variability data resulted in maximum F1-score of 87.67%.
Prolonged stress can negatively impact a person's mental health leading to multiple diseases. Stress monitoring can be efficiently done with the help of artificial intelligence technology combined with the benefits of quantum computing. The main aim of the paper is to analyze quantum enhanced machine learning techniques in predicting the stress of knowledge workers at office through multiple modalities. A general overview of popular quantum enhanced machine learning methods such as Quantum Support Vector Machine (QSVM), Variational Quantum Classifier (VQC) and Quantum K-Nearest Neighbor (QKNN) methods has been presented after studying the literatures of past 10 years. Besides, these models have been implemented on multimodal SWELL-KW dataset, which contains knowledge worker's computer interaction, facial expressions, body postures, heart rate variability and skin conductance data recorded in various working conditions. Further, the impacts of Quantum Principal Component Analysis based feature reduction on their performances have been analyzed. Experimental results show that for the current dataset, QSVM model with PCA on heart rate variability and skin conductance data results in highest accuracy of 0.8.
Chronic stress may have detrimental impacts on mental health as well as an elevated risk of various diseases. Stress monitoring might become simple with the help of artificial intelligence and quantum computing. The main goal of this work is to investigate how quantum enhanced machine learning algorithms can be used to forecast the stress levels of knowledge workers in the workplace using many modalities. This paper provides a high-level overview of the most well-known quantum augmented machine learning algorithms., such as Variational Quantum Classifier (VQC)., Quantum K-Nearest Neighbor (QKNN) and Quantum Support Vector Machine (QSVM)., after analysing the literature during the last ten years
The quantification of physical activity energy expenditure (PAEE) offers significant benefits for healthcare monitoring and has the potential to promote healthy and active aging for elderly individuals. With recent advancements in quantum information and computation, quantum machine learning (QML) has emerged as a powerful tool capable of improving upon the measurement of PAEE. In this study, we propose a hybrid machine-learning model to predict PAEE. This model specifically leverages a classical long short-term memory (LSTM) model integrated with a variational quantum circuit (VQC). This model, which we refer to as the enhanced quantum long short-term memory linear (eQLSTML) model, was subsequently trained and tested using the publicly available GOTOV Human Physical Activity and Energy Expenditure Dataset for Older Individuals. Upon performance comparisons between the classical LSTM and proposed eQLSTML models, our findings suggest that the eQLSTML modeling approach demonstrates superior performance compared to classical machine learning methods, thereby holding a promise for personalized healthcare monitoring and promoting healthy aging in the older population.
The rapid development and integration of wearable technologies or Internet-of-Things (IoT) into healthcare technologies with computational methods holds transformative potential for future healthcare systems, especially for personalized health monitoring and early disease detection. In parallel, recent advancements in quantum computing have o↵ered significant interest in quantum machine learning (QML), an intersection of quantum computing and machine learning, which provides a novel hybrid computational paradigm capable of addressing complex healthcare challenges that remain unsolvable with classical machine learning techniques. This thesis explores the application of quantum-enhanced recurrent neural network architectures, specifically Quantum Long Short-Term Memory (QLSTM) and Quantum-based Gated Recurrent Unit (QGRU), for predictive modeling and activity classification in wearable IoT systems, focusing particularly on their applicability and efficacy to elderly populations. One of the key challenges in modern healthcare intelligence systems is the simple but accurate estimation mechanism of Physical Activity Energy Expenditure (PAEE), which is an essential factor for assessing physical health status, particularly for healthy aging. In order to address this challenge, the first work proposes a model called an enhanced QLSTM with linear layer (eQLSTML) that integrates Variational Quantum Circuits (VQCs) into classical LSTM architectures. This integration notably explores the advantages of quantum computing, including properties such as entanglement and superposition, to enhance the model’s ability to capture complex temporal dependencies and subtle variations in human activity patterns. The modelling experiments were evaluated using the publicly available GOTOV Human Physical Activity dataset, which includes accelerometer data collected from older adults engaged in various daily activities. In addition to modelling architecture analysis, this thesis provides comprehensive analyses concerning the scalability, feasibility, and computational complexity of the proposed quantum-enhanced models compared to the classical backbone. The overall results demonstrate that the proposed quantum-enhanced approach significantly outperforms traditional classical machine learning algorithms, which demonstrates the potential ability for accurate predictions and robustness to noisy data like accelerometer data. In addition, accurate classification of activities of daily living (ADLs) is another critical aspect for early disease detection and e↵ective healthcare intervention. Within the context of this thesis, the second work introduces a novel hybrid model called QGRU-Multiclass Classifier (QGRU-MC). The data preprocessing involves creating a statistical feature extraction and applying oversampling techniques – SMOTE-ENN (Synthetic Minority Oversampling Technique with Edited Nearest Neighbors) for the publicly available dataset called “Dataset for ADL recognition with Wrist-worn Accelerometer”. The preliminary findings suggest that our model has good potential for healthcare applications, particularly in advancing future intelligent systems focused on daily activity monitoring. Specifically, QGRU-MC achieved significant improvements in classification accuracy, sensitivity, and specificity across multiple categories of daily activities, highlighting its potential for practical deployment in intelligent wearable systems aimed at monitoring and supporting healthy aging. Furthermore, this work also discusses the future research directions and practical considerations for transforming from the simulators to modern real quantum computers in the noisy intermediate-scale quantum (NISQ) era for a more comprehensive view on exploiting potential impacts on public health policies and personalized healthcare systems. Overall, simulator-based results indicate promising potential for integrating QML methods into wearable healthcare applications such as predictive analytics, personalized treatments, and health disease intervention to improve healthcare outcomes. Specifically, on PAEE regression, the proposed eQLSTML improved R² by 13.2% and reduced RMSE and MAE by 14.8% and 19.1%, respectively, relative to the classical LSTM; on ADL, QGRU-MC demonstrates good macro-averaged metrics across all eight classes (see Chs. 3–4). This research contributes significantly to the growing body of literature supporting quantum computing technologies in healthcare applications, making QML an incredible tool for addressing complex healthrelated computational challenges in the era of big and complex data in medical fields and smart healthcare technologies, especially in e↵ectively solving data from multiple sources, wearable accelerometry, and multi-class ADL recognition with complicated tasks.
Physical activity energy expenditure (PAEE) offers significant benefits for general healthcare monitoring and has the potential to promote healthy and active aging for elderly individuals. With recent advancements in quantum information and computation, quantum machine learning (QML) has emerged as a tool capable of improving upon the measurement of PAEE. In this paper, we propose a hybrid QML model to predict PAEE which consists of a classical long short-term memory (LSTM) model integrated with a variational quantum circuit (VQC). This model, which we refer to as the enhanced quantum long short-term memory linear (eQLSTML), was subsequently trained and tested using the publicly available GOTOV Human Physical Activity and Energy Expenditure Dataset for Older Individuals. In particular, we study the proposed eQLSTML model with different gate choices in the quantum circuit along with various embedding and layering techniques. Our results indicate our model to be superior in both performance comparisons and prediction when compared to traditional machine learning methods currently employed. Our findings indicate that combining QML approaches with wearable IoT healthcare devices provides a new avenue for personalized healthcare monitoring and an effective method for promoting healthy aging.
Social media has become a significant resource for evaluating public attitudes regarding mental health, as individuals may express their thoughts through postings, comments, and conversations. Sentiment analysis is a promising method for studying emotional patterns expressed in mental-health-related text. Traditional machine learning for sentiment analysis faces challenges like transparency, scarce labeled data, and language limitations. This paper presents a sentiment classification (positive, negative, neutral) framework applied to mental-health-related Reddit posts. Repeated emotional patterns can offer helpful insights into sentiment trends in mental health discussions, but they shouldn’t be seen as proof of clinical disorders. In this paper, we propose a hybrid framework for sentiment classification from text, termed QBERT-LSTM, which integrates quantum-enhanced bidirectional encoder representations from transformers (QBERT) with long short-term memory (LSTM) networks. In the proposed approach, transformer models excel at capturing global context, while the LSTM enhances sequential patterns and temporal features. The quantum component uses a parameterized circuit with angle encoding and variational gates to perform nonlinear transformations on a quantum simulator (PennyLane). Subsequently, the model is tested on more intricate circuits comprising 2, 4, 6, 8, 10, and 12 qubits to assess its consistency. A novel Reddit dataset of mental health-related subreddits (anxiety, panic attacks, and depression) is constructed, with sentiment labels derived from the VADER sentiment analyzer. We thoroughly assessed the performance of our proposed model against deep learning models and cutting-edge transformer architectures, achieving up to 98% accuracy in the best-performing configuration on the generated dataset (mean accuracy 0.97±0.0059 across five random seeds).