Experimental study of shapelet based personalized human activity classification
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Human activity recognition (HAR) has many applications in both research and industry domains. It has a variety of applications in healthcare, cognitive assistance, and tracking, because the sensors are becoming both better and smaller to be used in a wearable device. Many HAR systems have been developed to detect human activities after the emergence of wearable motion sensors. But most use complex feature extraction methods to classify the activities to attain high accuracy. In this work, we use a waveform pattern matching approach that allows us to extract a small representative waveform for each activity called “Activity Shapelet” (“A-Shapelet”, for short). The focus of this work is to use a shapelet to accurately classify activities. The advantages of this method is to use raw data to classify activities. This eliminates the need for defining and distinguishing among different features of each activity for classifying them accurately. We provide important concepts related to shapelets and the process involved in the classification of activities. We give detailed analysis of accuracy results based on different parameters.