Date of Award
Bachelor of Engineering (Electrical)
School of Electrical, Computer and Telecommunications Engineering
Motion capture is currently a lively area of research in many disciplines, from sports science to medicine to entertainment. Though there are many different approaches that attempt to model and animate a human in a human environment there have been few attempts to classify the activities of a person through the use of motion capture, and fewer still using a single kinetic motion sensor. The goal of this thesis was to use a single motion sensor, able to detect three dimensional translation and three dimensional orientation, in order to classify the activities of a wearer. The crux of this thesis, however, is using the limited information gathered from the single strategically placed motion sensor and indentifying the unique characteristics of each type of activity across a range of different motions. The algorithm developed by this thesis computes the Time Series Bitmaps (TSBs) from the Symbolic Aggregate approXimation (SAX) of the motion capture data. To classify the motion data the TSBs were compared, using a Euclidean Distance algorithm, to the template TSBs created for each individual activity and the closest match found. The classification engine, conformed to the goals and limits expressed in the project scope. Thirteen different activities were classified, including a special „transitional‟ activity. The engine was able to classify data in a pseudo real-time manner as well as using pseudo streaming data. The accuracy ranged from 70% to 95% depending on whether the templates were generated from default or individual data. This outcome is competitive with previous forms of motion classification in terms of accuracy, however, it supersedes most of its predecessors in the fact that the algorithm developed can perform in real-time and handle streaming wireless data. This thesis is an important step in the development of a personal activity classification system. The end product would include the kinematic sensor worn on pelvis which streams data to software which, using the algorithm developed in this thesis, classifies the activities performed in real-time.