Efficient Algorithms for Accelerometer-Based Wearable Hand Gesture Recognition Systems

The rapid increase in the use of robotic systems in industrial and domestic environments makes it necessary the development of more natural interaction procedures. This paper presents the development of a user-specific hand Gesture Recognition System (GRS) based on the information of a single tri-axial accelerometer to recognize 7 different dynamic gestures for natural Human Machine Interaction (HMI). The aim of this paper is to analyze and compare different computational methods for feature extraction, dimensionality reduction, and vector classification in order to select the most suitable combination of signal processing stages that meets the performance requirements for a single-chip, wearable GRS system. These requirements are lag-free response, low size, and low power consumption while keeping high recognition accuracy. Experimental results show that the overall achievable accuracy can be up to 98% for Artificial Neural Network (ANN) and Extreme Learning Machine (ELM) predictors, and 99% for Support Vector Machines (SVM).