Mesquite MoCap: Democratizing Real-Time Motion Capture with Affordable, Bodyworn IoT Sensors and WebXR SLAM

2025-12-27

AI Summary

To address the high cost and complex deployment of professional optical motion capture systems, this paper proposes a low-cost, real-time inertial motion capture solution built natively on web technologies. The method integrates a 15-node IMU-based wearable sensor network with smartphone-mounted visual SLAM, introducing the first tightly coupled architecture combining WebXR SLAM and edge IMUs. The full-stack implementation leverages WebGL, WebSerial, WebSockets, and Progressive Web App (PWA) standards to enable zero-installation, cross-platform, browser-native execution. Open-source hardware and software designs reduce system cost to just 5% of commercial optical systems. Experimental evaluation demonstrates an average joint angle error of 2–5°, end-to-end latency under 15 ms at 30 FPS, packet loss rate ≤0.3%, and indoor tracking stability comparable to high-end optical systems.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Motion capture remains costly and complex to deploy, limiting use outside specialized laboratories. We present Mesquite, an open-source, low-cost inertial motion-capture system that combines a body-worn network of 15 IMU sensor nodes with a hip-worn Android smartphone for position tracking. A low-power wireless link streams quaternion orientations to a central USB dongle and a browser-based application for real-time visualization and recording. Built on modern web technologies -- WebGL for rendering, WebXR for SLAM, WebSerial and WebSockets for device and network I/O, and Progressive Web Apps for packaging -- the system runs cross-platform entirely in the browser. In benchmarks against a commercial optical system, Mesquite achieves mean joint-angle error of 2-5 degrees while operating at approximately 5% of the cost. The system sustains 30 frames per second with end-to-end latency under 15ms and a packet delivery rate of at least 99.7% in standard indoor environments. By leveraging IoT principles, edge processing, and a web-native stack, Mesquite lowers the barrier to motion capture for applications in entertainment, biomechanics, healthcare monitoring, human-computer interaction, and virtual reality. We release hardware designs, firmware, and software under an open-source license (GNU GPL).

Problem

• Democratizing motion capture with affordable IoT sensors and web technologies.
• Reducing cost and complexity for real-time motion capture outside labs.
• Enabling cross-platform, low-latency motion capture for diverse applications.

Innovation

• Low-cost inertial motion capture with body-worn IMU sensors
• Real-time browser-based system using WebXR and WebGL technologies
• Open-source hardware and software for cross-platform accessibility