# Personalized Wrist–Forearm Static Gesture Recognition Using the Vicara Kai Controller and Convolutional Neural Network

## TL;DR

This paper introduces a personalized gesture recognition system using a wearable controller and CNN, allowing users to define their own gestures for improved human-computer interaction.

## Contribution

The novelty lies in enabling user-defined gestures and adapting CNN parameters for high accuracy in personalized static gesture recognition.

## Key findings

- The system achieved a validation accuracy range of 0.88 to 0.94 and an average test-set accuracy of 0.92.
- The system demonstrated resilience with an AUC of 0.97 using the ROC method for gesture recognition.

## Abstract

Predefined, user-independent gesture sets do not account for individual differences in movement patterns and physical limitations. This study presents a personalized wrist–forearm static gesture recognition system for human–computer interaction (HCI) using the Vicara KaiTM wearable controller and a convolutional neural network (CNN). Unlike the system based on fixed, predefined gestures, the proposed approach enables users to define and train their own gesture sets. During gesture recording, users may either select a gesture pattern from a predefined prompt set or create their own natural, unprompted gestures. A dedicated software framework was developed for data acquisition, preprocessing, model training, and real-time recognition. The developed system was evaluated by optimizing the parameters of a lightweight CNN and examining the influence of sequentially applied changes to the input and network pipelines, including resizing the input layer, applying data augmentation, experimenting with different dropout ratios, and varying the number of learning samples. The performance of the resulting network setup was assessed using confusion matrices, accuracy, and precision metrics for both original gestures and gestures smoothed using the cubic Bézier function. The resulting validation accuracy ranged from 0.88 to 0.94, with an average test-set accuracy of 0.92 and macro precision of 0.92. The system’s resilience to rapid or casual gestures was also evaluated using the receiver operating characteristic (ROC) method, achieving an Area Under the Curve (AUC) of 0.97. The results demonstrate that the proposed approach achieves high recognition accuracy, indicating its potential for a range of practical applications.