Reducing the Response Time for Activity Recognition Through use of Prototype Generation Algorithms
The nearest neighbor approach is one of the most successfully deployed techniques used for sensor-based activity recognition. Nevertheless, this approach presents some disadvantages in relation to response time, noise sensitivity and high storage requirements. The response time and storage requirements are closely related to the data size. This notion of data size is an important issue in sensor-based activity recognition given the vast amounts of data produced within smart environments. A wide range of prototype generation algorithms, which are designed for use with the nearest neighbor approach, have been proposed in the literature to reduce the size of the data set. These algorithms build new artificial prototypes, which represent the data, and subsequently lead to an increase in the accuracy of the nearest neighbor approach. In this work, we discuss the use of prototype generation algorithms and their effect on sensor-based activity recognition using the nearest neighbor approach to classify activities, reducing the response time. A range of prototype generation algorithms based on positioning adjustment, which reduce the data size, are evaluated in terms of accuracy and reduction. These approaches have been compared with the normal nearest neighbor approach, achieving similar accuracy and reducing the data size. Analysis of the results attained provide the basis for the use of prototype generation algorithms for sensor-based activity recognition to reduce the overall response time of the nearest neighbor approach.
KeywordsActivity recognition Data-driven Nearest Neighbor (NN) Prototype Generation (PG) Response time
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