Classification of Transition Human Activities in IoT Environments via Memory-Based Neural Networks
Department of Physics “Ettore Pancini”, University of Naples Federico II, Complesso di Monte Sant’Angelo, Via Cintia 21, 80126 Napoli, Italy
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Electronics 2020, 9(3), 409; https://doi.org/10.3390/electronics9030409
Received: 31 December 2019
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Revised: 10 February 2020
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Accepted: 14 February 2020
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Published: 28 February 2020
(This article belongs to the Special Issue Recent Machine Learning Applications to Internet of Things (IoT))
Human activity recognition is a crucial task in several modern applications based on the Internet of Things (IoT) paradigm, from the design of intelligent video surveillance systems to the development of elderly robot assistants. Recently, machine learning algorithms have been strongly investigated to improve the recognition task of human activities. Though, in spite of these research activities, there are not so many studies focusing on the efficient recognition of complex human activities, namely transitional activities, and there is no research aimed at evaluating the effects of noise in data used to train algorithms. In this paper, we bridge this gap by introducing an innovative activity recognition system based on a neural classifier endowed with memory, able to optimize the performance of the classification of both transitional and non-transitional human activities. The system recognizes human activities from unobtrusive IoT devices (such as the accelerometer and gyroscope) integrated in commonly used smartphones. The main peculiarity provided by the proposed system is related to the exploitation of a neural network extended with short-term memory information about the previous activities’ features. The experimental study proves the reliability of the proposed system in terms of accuracy with respect to state-of-the-art classifiers and the robustness of the proposed framework with respect to noise in data.
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Keywords:
internet of things; human activity recognition; machine learning; neural networks; sensors
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MDPI and ACS Style
Acampora, G.; Minopoli, G.; Musella, F.; Staffa, M. Classification of Transition Human Activities in IoT Environments via Memory-Based Neural Networks. Electronics 2020, 9, 409. https://doi.org/10.3390/electronics9030409
AMA Style
Acampora G, Minopoli G, Musella F, Staffa M. Classification of Transition Human Activities in IoT Environments via Memory-Based Neural Networks. Electronics. 2020; 9(3):409. https://doi.org/10.3390/electronics9030409
Chicago/Turabian StyleAcampora, Giovanni, Gianluca Minopoli, Francesco Musella, and Mariacarla Staffa. 2020. "Classification of Transition Human Activities in IoT Environments via Memory-Based Neural Networks" Electronics 9, no. 3: 409. https://doi.org/10.3390/electronics9030409
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