RECOGNITION OF ACTIONS IN DAILY LIFE AND ITS PERFORMANCE ADJUSTMENT BASED ON SUPPORT VECTOR LEARNING

Abstract

This paper presents a recognition method for human actions in daily life. The system deals with actions related to regular human activity such as walking or lying down. The main features of the proposed method are: (i) simultaneous recognition, (ii) expressing lack of clarity in human recognition, (iii) defining similarities between two motions by utilizing kernel functions derived from expressions of actions based on human knowledge, (iv) robust learning capability based on support vector machine. Comparison with neural networks optimized by a back propagation algorithm and decision trees generated by C4.5 proves that the accuracy of recognition in the proposed method is superior to others. Recognizing actions in daily life robustly is expected to ensure smooth communication between humans and robots and to enhance support functionality in intelligent systems.