An Experimental Analysis of Various Machine Learning Algorithms for Hand Gesture Recognition

Abstract

Nowadays, hand gestures have become a booming area for researchers to work on. In communication, hand gestures play an important role so that humans can communicate through this. So, for accurate communication, it is necessary to capture the real meaning behind any hand gesture so that an appropriate response can be sent back. The correct prediction of gestures is a priority for meaningful communication, which will also enhance human–computer interactions. So, there are several techniques, classifiers, and methods available to improve this gesture recognition. In this research, analysis was conducted on some of the most popular classification techniques such as Naïve Bayes, K-Nearest Neighbor (KNN), random forest, XGBoost, Support vector classifier (SVC), logistic regression, Stochastic Gradient Descent Classifier (SGDC), and Convolution Neural Networks (CNN). By performing an analysis and comparative study on classifiers for gesture recognition, we found that the sign language MNIST dataset and random forest outperform traditional machine-learning classifiers, such as SVC, SGDC, KNN, Naïve Bayes, XG Boost, and logistic regression, predicting more accurate results. Still, the best results were obtained by the CNN algorithm.

A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors

“…A potential solution is adopting deep transfer learning using labeled data from other gesture recognition domains. [ 119 ] DL approaches usually work on classifying data from the same feature space, which require substantial amounts of data. In sensing modalities like sEMG, PPG, FMG, and IMU, transfer learning can be used to exploit auxiliary information by adjusting existing model parameters or reformulating the model to suit a new task, requiring fewer training iterations which can reduce the overall training time.…”

“…[ 118 ] It can learn from raw data without the need for handcrafted features which drops some of data preprocessing that is typically involved with ML. [ 119 ] However, using DL techniques for HGR recognition may not guarantee a better performance than conventional methods. There are various factors that should be considered when evaluating whether to adopt DL, including the number of datasets, the task complexity, and real‐time computational expenses.…”

A Sign Language Recognition System Applied to Deaf-Mute Medical Consultation

“…In the application field of sign language recognition, the main problem [ 39 ] at present is that running deep learning models has high requirements on the processor performance of the platform, so it cannot perform well on some low-power embedded platforms. The authors of Ref.…”

Hand gesture recognition using multi-sensor information fusion

“…Compared with other methods such as the keyboard, voice, and camera, gesture has the advantage of naturalness, directness, simplicity, high robustness, and high degree of portability and it has a rich and wide range of application scenarios such as entertainment, intelligent control, rehabilitation, and security. Therefore, it is of great practical significance to accurately recognize gestures [2,3,4]. However, the diversity and complexity of hand gestures brings great challenges to gesture recognition, which has drawn significant attention from both industry and academia.…”