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- Ali Hamad, Rebeen, 1989-, et al.
(författare)
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Efficacy of Imbalanced Data Handling Methods on Deep Learning for Smart Homes Environments
- 2020
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Ingår i: SN Computer Science. - Heidelberg : Springer Berlin/Heidelberg. - 2661-8907 .- 2662-995X. ; 1:4
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Tidskriftsartikel (refereegranskat)abstract
- Human activity recognition as an engineering tool as well as an active research field has become fundamental to many applications in various fields such as health care, smart home monitoring and surveillance. However, delivering sufficiently robust activity recognition systems from sensor data recorded in a smart home setting is a challenging task. Moreover, human activity datasets are typically highly imbalanced because generally certain activities occur more frequently than others. Consequently, it is challenging to train classifiers from imbalanced human activity datasets. Deep learning algorithms perform well on balanced datasets, yet their performance cannot be promised on imbalanced datasets. Therefore, we aim to address the problem of class imbalance in deep learning for smart home data. We assess it with Activities of Daily Living recognition using binary sensors dataset. This paper proposes a data level perspective combined with a temporal window technique to handle imbalanced human activities from smart homes in order to make the learning algorithms more sensitive to the minority class. The experimental results indicate that handling imbalanced human activities from the data-level outperforms algorithms level and improved the classification performance. © The Author(s) 2020
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| 2. |
- Ali Hamad, Rebeen, 1989-, et al.
(författare)
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Efficient Activity Recognition in Smart Homes Using Delayed Fuzzy Temporal Windows on Binary Sensors
- 2020
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Ingår i: IEEE journal of biomedical and health informatics. - Piscataway : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 24:2, s. 387-395
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Tidskriftsartikel (refereegranskat)abstract
- Human activity recognition has become an active research field over the past few years due to its wide application in various fields such as health-care, smart home monitoring, and surveillance. Existing approaches for activity recognition in smart homes have achieved promising results. Most of these approaches evaluate real-time recognition of activities using only sensor activations that precede the evaluation time (where the decision is made). However, in several critical situations, such as diagnosing people with dementia, “preceding sensor activations” are not always sufficient to accurately recognize the inhabitant's daily activities in each evaluated time. To improve performance, we propose a method that delays the recognition process in order to include some sensor activations that occur after the point in time where the decision needs to be made. For this, the proposed method uses multiple incremental fuzzy temporal windows to extract features from both preceding and some oncoming sensor activations. The proposed method is evaluated with two temporal deep learning models (convolutional neural network and long short-term memory), on a binary sensor dataset of real daily living activities. The experimental evaluation shows that the proposed method achieves significantly better results than the real-time approach, and that the representation with fuzzy temporal windows enhances performance within deep learning models. © Copyright 2020 IEEE
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