engleski

Off-line Inertial-Sensor Based Hand Gesture Recognition and Evaluation

Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures with computer programs and many different algorithms. Any bodily motion or state that most commonly originates from the face and/or hand can be interpreted as a human gesture. Most of the research today focuses on emotion detection and recognition of hand gestures using cameras and computer vision algorithms. Gesture recognition can be seen as the way computers begin to understand human body language. There are many different areas this topic of computer science can be applied to ; the main field is human-computer interaction interfaces (HCI). Today the main interaction tools between computers and humans are still keyboard and mouse. Gesture recognition can be used as a tool for communication with the machine and interact without any mechanical device such as keyboard or mouse. In this paper, we present the results of a comparison of five different machine learning classifiers in the task of human hand gestures recognition. Gestures were recorded by using inertial sensors, gyroscopes, and accelerometers placed at the wrist and index finger. One thousand and eight hundred (1800) hand gestures were recorded and labelled. Six important features were defined, for the identification of nine different hand gestures, using five different machine learning classifiers: Logistic Regression, Random Forests, Support Vector Machine (SVM) with linear kernel, Naïve Bayes classifier, and Stochastic Gradient Descent.

hand gestures, inertial sensors, machine learning algorithms, off-line classification, evaluation

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