# Machine learning-enhanced gesture recognition through impedance signal analysis

## TL;DR

This paper introduces a new method combining impedance signal analysis and machine learning to improve gesture recognition accuracy in virtual reality and healthcare.

## Contribution

The novel integration of impedance signal spectrum analysis with multiple machine learning algorithms for gesture recognition.

## Key findings

- Machine learning algorithms achieved high accuracy (84-89%) in recognizing diverse hand gestures.
- Impedance signal features significantly enhanced gesture recognition precision.
- The model demonstrated adaptability across different conditions and subtle gesture variations.

## Abstract

Gesture recognition is a crucial aspect in the advancement of virtual reality, healthcare, and human-computer interaction, and requires innovative methodologies to meet the increasing demands for precision. This paper presents a novel approach that combines Impedance Signal Spectrum Analysis (ISSA) with machine learning to improve gesture recognition precision. A diverse dataset that included participants from various demographic backgrounds (five individuals) who were each executing a range of predefined gestures. The predefined gestures were designed to encompass a broad spectrum of hand movements, including intricate and subtle variations, to challenge the robustness of the proposed methodology. The machine learning model using the K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms demonstrated notable precision in performance evaluations. The individual accuracy values for each algorithm are as follows: KNN, 86%; GBM, 86%; NB, 84%; LR, 89%; RF, 87%; and SVM, 87%. These results emphasize the importance of impedance features in the refinement of gesture recognition. The adaptability of the model was confirmed under different conditions, highlighting its broad applicability.