Measuring Fine Motor Control in XR

Overview | Metrics | System | Sensing | Installation | History | Team

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Table of Contents

• Overview
• Metrics
• System
• Sensing
• Current Study
• Data Output
• Installation
• Repository Structure
• History
• References
• Team
• License

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Overview

Sollertia is a system for measuring fine motor control in XR using task-based interaction and wearable force input.

It replicates the same button-based task in a physical setup and in XR, and compares performance across both environments.

Users respond to light-up targets by pressing them as quickly and accurately as possible. We log behavioral and force data to capture how people move, react, and apply pressure during interaction.

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Metrics

Behavioral (XR + Physical)

Metric	Description
Reaction Time	Time from target onset to response
Completion Time	Total time to complete press
Spatial Error	Distance from target center
Variability	Consistency across trials
Movement Trajectory	Path of hand/finger during reach

Wearable (FSR on Index Finger)

Metric	Description
Force Magnitude	Peak pressure during press
Force Over Time	Temporal force profile
Pressure Variability	Stability of force application

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System

Component	Description
Physical Board	LED targets with matched layout
XR Version	Meta Quest 3 with hand tracking, matched timing
FSR Sensor	Finger-mounted force sensing
Logging Pipeline	Synchronized timing and force data

Architecture

                    +------------------+
                    |   Meta Quest 3   |
                    |   XR Application |
                    +--------+---------+
                             |
                             | WebSocket
                             |
+------------------+         |         +------------------+
|    Wearable      +---------+---------+    Dashboard     |
|    Hardware      |   USB Serial      |    (Rust)        |
|    (FSR)         |                   |                  |
+------------------+                   +------------------+

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Sensing

Current

• FSR sensor on index finger to measure press force and pressure over time

In Progress

• EMG integration using OpenBCI and BrainFlow to capture muscle activation during interaction

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Current Study

We compare performance between a physical task and its XR equivalent to evaluate whether XR interaction can capture meaningful fine motor behavior.

Direction: This project focuses on measuring motor behavior in controlled tasks.

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Data Output

Each trial logs:

Field	Description
target_id	Which target was pressed
stimulus_time	When target lit up
press_time	When press was registered
reaction_time	Time to respond
accuracy	Spatial error from center
trajectory	Movement path (XR only)
force_signal	FSR readings over time

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Installation

Prerequisites

• Unity 6 (6000.3.7f1 or later)
• Rust toolchain (1.70+)
• Arduino IDE (2.0+)
• Meta Quest 3 with developer mode enabled

XR Application

1. Clone the repository:

   git clone https://github.com/anaya33/Sollertia.git
   cd Sollertia

2. Open game/ in Unity Hub

3. Install required packages:

   • XR Interaction Toolkit
   • OpenXR Plugin
   • XR Hands

4. Open Assets/SollertiaDemo.unity and press Play

For Quest 3 deployment, see QUEST_DEPLOYMENT.md.

Hardware

FSR (Index) → A0

Upload hardware/hardware.ino via Arduino IDE.

Dashboard

cd dashboard
cargo build --release
cargo run --release

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Repository Structure

Sollertia/
|
|-- game/                    # Unity XR application
|   |-- Assets/
|   |   |-- Scripts/Sollertia/
|   |   |-- SollertiaDemo.unity
|
|-- dashboard/               # Rust dashboard
|   |-- Cargo.toml
|   |-- crates/
|
|-- hardware/                # Arduino firmware
|   |-- hardware.ino
|
|-- paper/                   # Research whitepaper (LaTeX)
|
|-- docs/                    # GitHub Pages site
|
|-- README.md
|-- LICENSE

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History

Origin: UGAHacks XI

Sollertia began as a hackathon project at UGAHacks XI The original vision was a mixed-reality rehabilitation tool for stroke recovery, combining XR interaction with wearable pressure sensing.

Original Hackathon Team:

• Anaya Yorke
• David Salas C.
• Garret Stand
• Mathias Sosa

Evolution to Research

After the hackathon, the project evolved from a rehabilitation-focused prototype into a research system for studying fine motor control. The scope shifted from clinical rehabilitation to fundamental research on comparing motor behavior across physical and XR environments.

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References

1. Schoen et al., 2025. From Pegs to Pixels: A Comparative Analysis of the Nine Hole Peg Test and a Digital Copy Drawing Test for Fine Motor Control Assessment. PDF

2. Lu et al., 2019. Modeling Endpoint Distribution of Pointing Selection Tasks in Virtual Reality Environments. PDF

3. Chen et al., 2024. Metrics of Motor Learning for Analyzing Movement Mapping in Virtual Reality. PDF

4. Wei et al., 2019. Accurate and Low-Latency Sensing of Touch Contact on Any Surface with Finger-Worn IMU Sensor. PDF

5. Schneider et al., 2021. Accuracy Evaluation of Touch Tasks in Commodity Virtual and Augmented Reality Head-Mounted Displays. PDF

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Team

Current Research Team

Name	Role	GitHub
Anaya Yorke	Researcher	@anaya33
Garret Stand	Researcher	@gstand

Original Hackathon Team (UGAHacks XI)

• Anaya Yorke
• David Salas C.
• Garret Stand
• Mathias Sosa

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License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

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Sollertia - Latin for "skill with hand"

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