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Optimizing Interaction Space: Enlarging the Capture Volume for Multiple Portable Motion Capture Devices

Muhammad Hilman Fatoni, Christopher Herneth, Junnan Li, Fajar Budiman, Amartya Ganguly, Sami Haddadin

TL;DR

This study employs four LMC devices to optimize their collective tracking volume, aiming to enhance the accuracy and precision of hand kinematics, and determines an optimized layout for the four LMC devices through Monte Carlo simulation.

Abstract

Markerless motion capture devices such as the Leap Motion Controller (LMC) have been extensively used for tracking hand, wrist, and forearm positions as an alternative to Marker-based Motion Capture (MMC). However, previous studies have highlighted the subpar performance of LMC in reliably recording hand kinematics. In this study, we employ four LMC devices to optimize their collective tracking volume, aiming to enhance the accuracy and precision of hand kinematics. Through Monte Carlo simulation, we determine an optimized layout for the four LMC devices and subsequently conduct reliability and validity experiments encompassing 1560 trials across ten subjects. The combined tracking volume is validated against an MMC system, particularly for kinematic movements involving wrist, index, and thumb flexion. Utilizing calculation resources in one computer, our result of the optimized configuration has a better visibility rate with a value of 0.05 $\pm$ 0.55 compared to the initial configuration with -0.07 $\pm$ 0.40. Multiple Leap Motion Controllers (LMCs) have proven to increase the interaction space of capture volume but are still unable to give agreeable measurements from dynamic movement.

Optimizing Interaction Space: Enlarging the Capture Volume for Multiple Portable Motion Capture Devices

TL;DR

This study employs four LMC devices to optimize their collective tracking volume, aiming to enhance the accuracy and precision of hand kinematics, and determines an optimized layout for the four LMC devices through Monte Carlo simulation.

Abstract

Markerless motion capture devices such as the Leap Motion Controller (LMC) have been extensively used for tracking hand, wrist, and forearm positions as an alternative to Marker-based Motion Capture (MMC). However, previous studies have highlighted the subpar performance of LMC in reliably recording hand kinematics. In this study, we employ four LMC devices to optimize their collective tracking volume, aiming to enhance the accuracy and precision of hand kinematics. Through Monte Carlo simulation, we determine an optimized layout for the four LMC devices and subsequently conduct reliability and validity experiments encompassing 1560 trials across ten subjects. The combined tracking volume is validated against an MMC system, particularly for kinematic movements involving wrist, index, and thumb flexion. Utilizing calculation resources in one computer, our result of the optimized configuration has a better visibility rate with a value of 0.05 0.55 compared to the initial configuration with -0.07 0.40. Multiple Leap Motion Controllers (LMCs) have proven to increase the interaction space of capture volume but are still unable to give agreeable measurements from dynamic movement.
Paper Structure (15 sections, 5 equations, 5 figures, 2 tables)

This paper contains 15 sections, 5 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Materials and Methods
  3. Subject Information
  4. Markerless Motion Capture System
  5. Marker Based Motion Capture System
  6. Experimental Protocol
  7. LMC Placement Optimization
  8. Data Collection
  9. Data Processing
  10. Results
  11. Optimized LMCs Configuration
  12. Validity and Repeatability
  13. Visibility Rate
  14. Discussion
  15. Conclusion

Figures (5)

  • Figure 1: Multiple LMCs validation pipeline for initial and optimized LMC placement. LMC data is captured - interpolated - realigned - Kalman filtered. Comparisons are made to ground truth optical marker trajectories from MMC.
  • Figure 2: Experiment setup. Left: Simultaneous recording of MMC and LMCs system. Markers were placed on the hand while an experiment of index flexion movement in the vertical pose of optimized LMCs configuration was running. The cue recording is a big red circle shape, informing the subject to start the trial. Right: Visualization of LMCs marker reading using OpenGL in developed custom program.
  • Figure 3: Optimization procedure: 1: generation of input data; 2 - 4: optimization with ray-tracing algorithm. The bottom shows exemplary LMCs ray-tracing for the tip of the middle finger from optimized positions (blue) and initial positions (red).
  • Figure 4: (a) Average inter-subject finger length for all static trial of each LMC and MMC in two configurations and hand poses. (b) Average inter-subject joint angle for all dynamic trials of each LMC and MMC in two configurations and hand poses.
  • Figure 5: Map of mean visibility rate from ten subjects for each LMC. Visibility rate value: -1: Hand not detected, 0: Finger marker detected from LMC internal model, 1: Finger marker truly detected from ray tracing.