Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Sense4HRI: A ROS 2 HRI Framework for Physiological Sensor Integration and Synchronized Logging

Manuel Scheibl, Julian Leichert, Sinem Görmez, Britta Wrede

Abstract

Physiological signals are increasingly relevant to estimate the mental states of users in human-robot interaction (HRI), yet ROS 2-based HRI frameworks still lack reusable support to integrate such data streams in a standardized way. Therefore, we propose Sense4HRI, an adapted framework for human-robot interaction in ROS 2 that integrates physiological measurements and derived user-state indicators. The framework is designed to be extensible, allowing the integration of additional physiological sensors, their interpretation, and multimodal fusion to provide a robust assessment of the mental states of users. In addition, it introduces reusable interfaces for timestamped physiological time-series data and supports synchronized logging of physiological signals together with experiment context, enabling interoperable and traceable multimodal analysis within ROS 2-based HRI systems.

Sense4HRI: A ROS 2 HRI Framework for Physiological Sensor Integration and Synchronized Logging

Abstract

Physiological signals are increasingly relevant to estimate the mental states of users in human-robot interaction (HRI), yet ROS 2-based HRI frameworks still lack reusable support to integrate such data streams in a standardized way. Therefore, we propose Sense4HRI, an adapted framework for human-robot interaction in ROS 2 that integrates physiological measurements and derived user-state indicators. The framework is designed to be extensible, allowing the integration of additional physiological sensors, their interpretation, and multimodal fusion to provide a robust assessment of the mental states of users. In addition, it introduces reusable interfaces for timestamped physiological time-series data and supports synchronized logging of physiological signals together with experiment context, enabling interoperable and traceable multimodal analysis within ROS 2-based HRI systems.
Paper Structure (5 sections, 3 figures, 1 table)

This paper contains 5 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Design
  4. Proof of Concept
  5. Conclusion

Figures (3)

  • Figure 1: Schematic overview of the physiological sensing pipeline used in this work. Left: exemplary body locations of the considered sensor modalities. Middle: corresponding primary (raw) measurements. Right: high-level indicator categories for which each modality can provide evidence. Crosses indicate a non-exclusive mapping (i.e., a sensor may inform multiple categories and categories may be supported by multiple sensors).
  • Figure 2: Overview of the proposed ROS 2 architecture for physiological sensing (left) and detailed structure of PhysioRaw.msg (right). A sensor driver node wraps the physiological device and publishes raw measurements as PhysioRaw.msg, which can be consumed directly by applications or processed by an interpreter node for sensor-specific preprocessing and feature extraction. The architecture further supports device-level feature messages (PhysioDeviceFeature.msg) and processed sensor-specific outputs (<SensorType>.msg). The PhysioRaw.msg message comprises ROS metadata (header), a device-side timestamp (device_timestamp), and an array of PhysioRawChannel elements containing channel names and floating-point sample arrays.
  • Figure 3: Web GUI for real-time physiological sensor data visualization.