Convolutional neural network classifier with fuzzy feature representation for human activity modelling

Mohmed, G., Lotfi, A. ORCID: 0000-0002-5139-6565 and Pourabdollah, A. ORCID: 0000-0001-7737-1393, 2020. Convolutional neural network classifier with fuzzy feature representation for human activity modelling. .

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Abstract

Human activity recognition is concerned with identifying the specific movement of a person based on sensor data. In recent years, many different techniques have been proposed for modelling and recognising human activities, with a specific focus on the development of approaches to classifying human activities using deep learning techniques. The research presented in this paper proposes a fuzzy feature representation approach to represent occupancy sensor data, along with a Convolutional Neural Network Classifier (CNNC) for human activity modelling and recognition. Sensory data gathered from a home environment are converted into occupancy data representing human activities and then fuzzified before being fed as inputs into the CNNC. The learning capability of CNNC allows the model to learn the relationship between the fuzzified inputs and their corresponding output activities during training mode. The relations learned in the trained CNNC model are then used to identify human activity patterns and classify these when the testing dataset is applied. The proposed method is evaluated using a dataset representing activities of daily living for a single user gathered from a real-home environment.

Item Type: Journal article
Description: Paper presented at the 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, Scotland, 19-24 July 2020.
Creators: Mohmed, G., Lotfi, A. and Pourabdollah, A.
Publisher: IEEE
Place of Publication: Piscataway, NJ
Date: 2020
ISBN: 9781728169323
Identifiers:
NumberType
10.1109/fuzz48607.2020.9177851DOI
1358639Other
Divisions: Schools > School of Science and Technology
Record created by: Jonathan Gallacher
Date Added: 01 Sep 2020 09:19
Last Modified: 01 Sep 2020 09:54
URI: http://irep.ntu.ac.uk/id/eprint/40601

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