| Summary: | INTRODUCTION: Neural networks are a popular type of algorithm for human activity monitoring which canbuild intelligent systems from labelled data in an automated fashion. Obtaining accurately labelled data is costly; it requires time and effort, which can be cumbersome because it interrupts the user activity stream. In conjunction with the ubiquitous presence of embedded technology, neural networks present new research opportunities for human activity monitoring in smart home environments.OBJECTIVES: We propose a human activity classification method that requires a limited amount oflabelled data, which consists of a concatenation method for classifying human activities built upon the fusion of neural network activation functions.METHODS: Our methodology builds a neural network model that receives the sensor data through the inputlayer to then distribute it among the different vertical hidden layers, which implement different activationfunctions simultaneously. Next a hidden layer combines activation functions by utilising a concatenationmethod. Finally, the neural network provides classes to the unlabelled sensing data. We conducted anevaluation utilising an open-access dataset. We compared the activity recognition accuracy of our approach utilising 25%, 50%, and 75% of labelled data against a conventional shallow neural network trained with the 100% of labelled data available.RESULTS: Results show an improvement in the accuracy of the activity classification regardless of the portion of labelled data available. It was observed that the highest achieved accuracy when using 25% of activation function fusion data outperformed results compared to when using 100% of labelled data in a conventional shallow network (i.e., increase in accuracy of 2.7%, 3.7%, 4.8%, and 0.9% across the activity recognition of four subjects).CONCLUSION: The approach proposed showed an improvement in the accuracy of classifying humanactivity when a limited amount of labelled data is available.
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