Abstract
Heart dysfunction can be very dangerous and directly associated with the risk of fatality. Therefore, automatic heart rate monitoring is important for detecting a heart abnormality early and providing timely intervention. The development of advanced sensor technology allows continuous tracking of the heart rate, as well as providing more health-related data sources, which could further improve the forecasting performance. In this study, we investigate the use of recent heart rate values and Ultra-Wideband (UWB) data in the last 20s to predict the next 10s of heart rate. Data were collected from 20 participants performing different activities. We examine the forecasting by using only a heart rate sensor (uni-modality) and by using heart rate sensor and UWB together (multi-modality). Three levels of activity were included in the study: resting (rest), lying after doing exercises (lying-abnormal) and lying after full recovery (lying-normal). A multi-modality Sequence-to-Sequence (Seq2Seq) model has a 10-percent quantile losses of 5.20 for rest activity, 4.92 for lying-abnormal activity, and 4.24 for lying-normal activity. With the lowest loss, Seq2Seq outperforms Autoregressive Integrated Moving Average (ARIMA), Error Trend and Seasonality (ETS), Transformer and Temporal Fusion Transformer (TFT) in forecasting heart rate in the three activity levels. Our results point to a new direction in multimodal prediction of heart rate to assist healthcare, using deep learning approaches.