PhysioSense: An Open Source Multimodal Monitoring Framework for Human Movement and Behavior Analysis

Abstract

Accurate assessment of human movement and behavior is essential in fields such as ergonomics, rehabilitation, and human-robot interaction. This paper presents PhysioSense, an open-source framework for synchronized multi-modal data acquisition and management. Built on the Lab Streaming Layer (LSL), PhysioSense integrates heterogeneous data streams from kinematic, dynamic, and physiological sensors in real time, ensuring millisecond-level synchronization. Unlike general-purpose tools such as LabVIEW, OpenSignals, or ROS, PhysioSense is specifically tailored to human-centric research, offering a streamlined interface for sensor configuration, recording, visualization, and data export. The framework’s modular design supports extensibility and reproducibility, making it suitable for a range of experimental setups. Two case studies—an ergonomics analysis and a drilling task assessment—demonstrate the framework’s capabilities in real-world scenarios. PhysioSense addresses key challenges in multi-sensor integration and paves the way for more accessible and scalable movement analysis in both research and applied settings.