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Personalized Wrist-Forearm Static Gesture Recognition Using the Vicara Kai Controller and Convolutional Neural

Jacek Szedel1

  • 1Department of Algorithmics and Software, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary

This study introduces a personalized gesture recognition system for human-computer interaction (HCI). It allows users to train custom gestures using a wearable controller and a convolutional neural network (CNN), achieving high accuracy.

Keywords:
Vicara Kai controllerconvolutional neural networkspersonalized gesture recognition

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Area of Science:

  • Human-Computer Interaction (HCI)
  • Machine Learning
  • Wearable Technology

Background:

  • Existing gesture recognition systems often use predefined, user-independent sets, failing to accommodate individual movement variations.
  • Personalized gesture recognition is crucial for effective and intuitive human-computer interaction (HCI).

Purpose of the Study:

  • To develop and evaluate a personalized wrist-forearm static gesture recognition system for HCI.
  • To enable users to define and train their own gesture sets using a wearable controller and a convolutional neural network (CNN).

Main Methods:

  • Utilized the Vicara Kai™ wearable controller for data acquisition.
  • Developed a software framework for data preprocessing, model training, and real-time recognition.
  • Optimized a lightweight CNN by adjusting input layers, data augmentation, dropout ratios, and learning sample size.

Main Results:

  • Achieved a validation accuracy ranging from 0.88 to 0.94, with an average test-set accuracy of 0.92.
  • Reported a macro precision of 0.92 and an Area Under the Curve (AUC) of 0.97 for gesture recognition.
  • Demonstrated high recognition accuracy for both original and smoothed gestures, including rapid or casual movements.

Conclusions:

  • The proposed personalized gesture recognition system offers high accuracy and robustness.
  • The system has significant potential for practical applications in human-computer interaction.
  • User-defined gestures enhance the intuitiveness and adaptability of HCI systems.