Quantum LSTM model for estimation of energy expenditure in human aging using wearable IoT healthcare technology

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.