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.
Highlights
By equipping environments such as ordinary homes with binary sensors for monitoring resident activities, a vast area of different applications is made possible, including smart monitoring of energy utilization and assessing resident situation and behavior pattern for proactive home care
Thereby, this paper aims to explore the potential enhancements of class imbalance approaches together with two deep learning models (1D convolutional neural networks (CNNs) and long short-term memory (LSTM)) and several pre-processing methods described in later sections
We investigate two types of neural networks: One is based on LSTM and another is based on CNN
Summary
By equipping environments such as ordinary homes with binary sensors for monitoring resident activities, a vast area of different applications is made possible, including smart monitoring of energy utilization and assessing resident situation and behavior pattern for proactive home care. SN Computer Science (2020) 1:204 ambulatory and postural activities, actions of residents and body movements using different multimodal data generated by heterogeneous sensors [5, 19, 31]. Are human activities highly diverse in the form of different sensor activations but the frequency of activities themselves is inherently imbalanced and accurate AR is challenging from a machine learning perspective. As an example, cooking may occur with a higher frequency than grooming. Another more prominent example is the vast difference in the number of examples between eating and sleeping where the latter occurs with a much higher frequency in datasets collected over a long duration. This paper focuses on investigating the problematic aspect of learning activities over days or even months which are imbalanced
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