Estimation of Energy Expenditure in Wearable Healthcare Technology by Quantum-Based LSTM Modeling (Invited Paper)

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.