A Transformer-Based Approach for Better Hand Gesture Recognition

Hand gesture recognition (HGR) is a vital area of research with widespread applications and employing disruptive technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT). HGR is very important in human-computer interaction (HCI), especially for people with disabilities. Several approaches have been described in the literature, including image-based, radar-based, and haptic-based ones. However, these approaches are difficult to implement in real time as they require high processing time (PT). Inertial sensors (IS) offer a worthy alternative for HGR and require much less PT. Several research papers have been published in this domain. However, models capturing complex temporal patterns using traditional machine learning (ML) algorithms have limitations. In this paper, we propose a transformer-based approach to accurately recognize dynamic gestures. A dataset we collected from accelerometer (ACC) and gyroscope (GYR) sensors was used to evaluate the performance of our approach. Our experimental results achieved an accuracy of 96.77% and outperformed traditional ML algorithms in terms of accuracy using the same dataset: Linear Discriminant Analysis (LDA) with 58.06%, KNearest Neighbor (KNN) with 70.97%, XGBoost (XGB) with 74.19%, Convolutional neural network (CNN) with 74.19%, Random Forest (RF) with 77.42%, and Support vector machine (SVM) with 93.55%. Furthermore, we implemented a real-time HGR system that can achieve a very short response time of less than 300 ms.