Please login first
Forecasting Vital Signs in Human-Robot Collaboration using Sequence-to-Sequence Models with Bidirectional LSTM: A Comparative Analysis of Uni- and Multivariate Approaches
* , , ,
1  Cologne Cobots Lab, TH Köln - University of Applied Sciences, 50679 Cologne, Germany
Academic Editor: Francisco Falcone


In the rapidly evolving landscape of human-robot collaboration (HRC), a challenge lies in endowing robotic systems with the capability to adapt seamlessly to users' internal states, such as stress or relaxation. Current research in the field demonstrates significant progress in accurately classifying stress through the integration of diverse sensors that monitor vital signs. However, there is limited investigation into predicting future states. By extending our understanding beyond the instantaneous recognition of emotional states to anticipatory modeling, robotic systems can proactively tailor their interactions, fostering a deeper, more intuitive, and ultimately more productive collaboration with human users.

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 while investigating their performance over different forecasting horizons ranging from 1 second to 10 seconds.

The dataset used in this research comprises sensor data collected during a lab study. Thirteen participants engaged in a collaborative assembly scenario with a collaborative robot, YuMi by ABB Robotics which is primarily used in industrial HRC assembly applications. The dataset includes diverse sensor modalities capturing heart rate (HR), pupil diameter (PD), and electrodermal activity (EDA). Prior to analysis, the data undergoes state-of-the-art preprocessing techniques. To evaluate forecasting accuracy, the Symmetric Mean Absolute Percentage Error (sMAPE) metric is utilized, maintaining consistent look-back and forecasting window lengths.

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 personalized medical monitoring, diagnostics, and healthcare applications.

Keywords: Human-Robot-Collaboration; Artificial Intelligence; Vital Signs; Forecasting; Deep Learning; LSTM