Forecasting Vital Signs in Human–Robot Collaboration Using Sequence-to-Sequence Models with Bidirectional LSTM: A Comparative Analysis of Uni- and Multi-Variate Approaches

Our research investigates an approach to forecasting human vital signs by formulating the problem as a sequence-to-sequence (seq2seq) task, utilizing bidirectional long short-term memory models (BiLSTM). The study aims to compare the forecasting accuracy of uni- and multivariate modeling strategies over different forecasting horizons ranging from 1 s to 10 s. The dataset comprises sensor data collected during a lab study in which thirteen participants engaged in a collaborative assembly scenario with a robot. Our results show that univariate models outperform multivariate ones in terms of forecasting accuracy, offering valuable insights into accurate forecasting of human physiological parameters, with potential implications for human-robot collaboration, personalized medical monitoring, and healthcare applications.