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

Poojan Vanani, Darsh Patel, Danyal Khorami, Siva Munaganuru, Pavan Reddy, Varun Reddy, Bhargav Raghunath, Ishrat Lallmamode, Romir Patel, Assegid Kidané, Tejaswi Gowda

TL;DR

Mesquite addresses the barrier of costly, lab-bound motion capture by delivering an open-source, IoT-based MoCap system built from 15 body-worn IMU pods and a hip-mounted smartphone. Real-time, browser-based visualization is achieved through WebXR SLAM with a lightweight central dongle and edge processing, exporting BVH and enabling offline use. The approach delivers 2–5 degree angular accuracy, under 5 cm positional drift over 10 minutes, 30 FPS operation, and end-to-end latency below 15 ms at a fraction of traditional costs, demonstrating strong potential for education, healthcare, biomechanics, and entertainment. By combining an affordable hardware stack with web-native software and a robust calibration/processing pipeline, Mesquite promises broad, community-driven adoption of motion capture across diverse domains.

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).

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

TL;DR

Mesquite addresses the barrier of costly, lab-bound motion capture by delivering an open-source, IoT-based MoCap system built from 15 body-worn IMU pods and a hip-mounted smartphone. Real-time, browser-based visualization is achieved through WebXR SLAM with a lightweight central dongle and edge processing, exporting BVH and enabling offline use. The approach delivers 2–5 degree angular accuracy, under 5 cm positional drift over 10 minutes, 30 FPS operation, and end-to-end latency below 15 ms at a fraction of traditional costs, demonstrating strong potential for education, healthcare, biomechanics, and entertainment. By combining an affordable hardware stack with web-native software and a robust calibration/processing pipeline, Mesquite promises broad, community-driven adoption of motion capture across diverse domains.

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).
Paper Structure (18 sections, 2 equations, 10 figures)

This paper contains 18 sections, 2 equations, 10 figures.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. IMU-Based Motion Capture
  4. IoT Sensor Networks for Human Motion Analysis
  5. Position and Orientation Estimation
  6. System Design
  7. Hardware Components
  8. Software Components
  9. Calibration Process
  10. Skeletal Model and Animation
  11. Data Export and Interoperability
  12. Other Considerations
  13. Evaluation
  14. Methodology
  15. Results
  16. ...and 3 more sections

Figures (10)

  • Figure 1: Overview of the Mesquite motion capture system showing (a)wearable sensor node (pod) placement on the human body. (b) Hipworn smartphone for spatial anchoring (c) Network architecture with a Raspberry Pi Zero as the central hub. (d) The system uses a standard Wi-Fi router to facilitate communication between the sensor nodes and the hub, enabling real-time data transmission and visualization. (e) The web-based interface allows users to visualize and record motion data in real-time.
  • Figure 2: The Mesquite sensor node ("pod") (a) i2c ICM20948 + ESP32 C3 (TTGO T-OI) + LiPo battery (b) pod in 3d-printed enclosure (c) Full pod in enclosure
  • Figure 3: All 15 pods are attached to the body using adjustable straps. The system is designed to be lightweight, unobtrusive, and one size fits all. The smartphone is mounted on the hip for spatial anchoring.
  • Figure 4: Mesquite dongle (Raspberry Pi Zero W Wi-Fi module) and Wifi router. The Raspberry Pi Zero W acts as a USB dongle and local HTTP and WebSocket server, while the Wi-Fi router facilitates communication between the sensor nodes and the hub.
  • Figure 5: WebXR SLAM: The smartphone uses WebXR World Mapping to establish an absolute spatial frame, which is then used to track the user's hip position. The system uses a standard Wi-Fi router to facilitate communication between the sensor nodes and the hub, enabling real-time data transmission and visualization.
  • ...and 5 more figures