Ashok Kumar Patil
ABSTRACT
This paper presents ongoing research work on developing a protocol framework for human motion recognition using complex and continuous 3D motion data into more intuitive 2D trajectory representation based-on the quaternion visualization. Quaternions are very compact and free from gimbal lock for representing orientations and rotations of objects in 3D space. In this study, the focus is only on the arm orientation and not the position. In our pilot experimental evaluation, we examine our approach to visually recognize several biceps curl using quaternions data collected using wireless inertial sensors attached to the human arm. The results of the analysis indicate that the proposed framework makes it possible to represent 3D motion data in the form of a 2D trajectory for continuous motion patterns.
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