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Human activity recognition has become an increasingly important field of research with many practical applications related to health care and leisure activities. The accessibility of inexpensive portable sensors, such as accelerometers, allows for a widespread use of this technology for both commercial and personal activity recognition. This paper develops a novel feature extraction approach to human activity recognition through the development of the Lempel-Ziv-Welch Coded Probabilistic Finite State Automata (LZW-Coded PFSA) to classify activities such as walking, jumping, running, waist rotations, and shoulder rotations. The PFSA reveal the underlying architecture of a given activity and classify it without making any a priori assumptions by inferring patterns from the sensor measurements. LZW-Coded PFSA select the optimal variable length state from the time-series data and compress it into class-separable state transition matrices π. This algorithm is robust to subject biases and is shown to be effective with a correct classification rate of 95.63%.

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