phat 19(17): e3

Research Article

Adoption of the activation function fusion approach to identify human activity recognition in a semi-supervised neural network

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  • @ARTICLE{10.4108/eai.30-10-2018.161669,
        author={Netzahualcoyotl  Hernandez and Chris  Nugent and Ian  McChesney and Shuai  Zhang and Jesus  Favela},
        title={Adoption of the activation function fusion approach to identify human activity recognition in a semi-supervised neural network},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={5},
        number={17},
        publisher={EAI},
        journal_a={PHAT},
        year={2019},
        month={2},
        keywords={activation function, data fusion, neural networks, activity recognition},
        doi={10.4108/eai.30-10-2018.161669}
    }
    
  • Netzahualcoyotl Hernandez
    Chris Nugent
    Ian McChesney
    Shuai Zhang
    Jesus Favela
    Year: 2019
    Adoption of the activation function fusion approach to identify human activity recognition in a semi-supervised neural network
    PHAT
    EAI
    DOI: 10.4108/eai.30-10-2018.161669
Netzahualcoyotl Hernandez1,*, Chris Nugent1, Ian McChesney1, Shuai Zhang1, Jesus Favela2
  • 1: Ulster University, Newtownabbey, Belfast, Northern Ireland, UK
  • 2: Ensenada Center for Scientific Research and Higher Education (CICESE), Baja California, Ensenada, Mexico
*Contact email: hernandez_cruz-n@ulster.ac.uk

Abstract

INTRODUCTION: Neural networks are a popular type of algorithm for human activity monitoring which can build 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 of labelled 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 input layer to then distribute it among the different vertical hidden layers, which implement different activation functions simultaneously. Next a hidden layer combines activation functions by utilising a concatenation method. Finally, the neural network provides classes to the unlabelled sensing data. We conducted an evaluation 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 human activity when a limited amount of labelled data is available.