Representing and Learning Human Behavior Patterns with Contextual Variability

Lago, Paula; Roncancio, Claudia; Jiménez-Guarín, Claudia; Labbé, Cyril

doi:10.1007/978-3-319-64468-4_23

Cited by 2 publications

(1 citation statement)

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“…In this section, we describe the results of our model obtained on the ContextAct@A4H dataset compared to other deep learning approaches proposed for recognizing ADLs using the aforementioned metrics. Since, as said previously, ContextAct@A4H is a newly collected dataset, it has been used in few studies for recognizing ADLs [32,33]. However, non-of them used it along with deep model to solve the complexity of activity recognition, thus, we could not compare our work with them.…”

Section: ) Comparative Results On Deep 1d-cnn With Other Proposed Deep Modelsmentioning

confidence: 99%

Recognizing Activities of Daily Living using 1D Convolutional Neural Networks for Efficient Smart Homes

Alghamdi¹,

Fadel²,

Alowidi³

2021

IJACSA

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Human activity recognition is considered a challenging task in sensor-based monitoring systems. In ambient intelligent environments, such as smart homes, collecting data from ambient sensors is useful for recognizing activities of daily living, which can then be used to provide assistance to inhabitants. Activities of daily living are composed of complex multivariable time series data that has high dimensionality, is huge in size, and is updated constantly. Thus, developing methods for analyzing time series data to extract meaningful features and specific characteristics would help solve the problem of human activity recognition. Based on the noticeable success of deep learning in the field of time series classification, we developed a model called a deep one-dimensional convolutional neural network (Deep 1d-CNN) for recognizing activities of daily living in smart homes. Our model contains several onedimensional convolution layers coupled with max-pooling technique to learn the internal representation of time series data and automatically generate very deep features for recognizing different activity types. For the performance evaluation, we tested our deep model on the new real-life dataset, ContextAct@A4H, and the results showed that our model achieved a high F1 score (0.90). We also extended our study to show the potential energy saving in smart homes through recognizing activities of daily living. We built a recommendation system based on the activities recognized by our deep model to detect the devices that are wasting energy, and recommend the user to execute energy optimization actions. The experiment indicated that recognizing activities of daily living can result in energy savings of around 50%.

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Section: ) Comparative Results On Deep 1d-cnn With Other Proposed Deep Modelsmentioning

confidence: 99%