A Low-Latency Embedded Inertial Motion Tracking System for Real-Time Applications

Inertial motion sensing plays an important role in real-time human–machine interaction applications, including interactive systems, virtual environments, and motion-controlled interfaces, where low latency and accurate motion tracking are important. This paper presents the design and implementation of an embedded inertial sensing system that acquires, processes, and transmits motion data from consumer-grade inertial measurement units (IMUs) under real-time constraints. Rather than introducing a new sensor fusion algorithm, the contribution lies in a systems-oriented methodology comprising a predictive clock-advancement mechanism that prevents cumulative timing drift, an automated matrix-based calibration procedure for hardware-agnostic deployment, and a benchmarking framework for end-to-end real-time system evaluation. Implemented on a resource-constrained embedded platform, the framework integrates sensor acquisition, lightweight filtering, sensor fusion, and real-time orientation estimation within a single processing pipeline. Motion data are transmitted using the CemuHook UDP (User Datagram Protocol) motion protocol (DSU) to demonstrate interoperability with existing motion-control software while maintaining low end-to-end latency and stable throughput. Experimental results show stable sampling frequency, low communication latency, accurate orientation estimation, and low computational overhead. The presented system provides an embedded inertial sensing framework that can be adapted to a range of real-time motion-sensing applications beyond the communication protocol used for demonstration.