Activity recognition in smart homes is essential when we wish to propose automatic services
for the inhabitants. However, it is a challenging problem in terms of environments' variability …

The performance of deep neural networks is strongly influenced by the quantity and quality
of annotated data. Most of the large activity recognition datasets consist of data sourced from …

Daily activity recognition between different smart home environments faces some
challenges, such as an insufficient amount of data and differences in data distribution …

Activity recognition is an important step towards monitoring and evaluating the functional
health of an individual, and it potentially promotes human-centric ubiquitous applications in …

Multi-sensor based human activity recognition is one of the challenges in the ambient
intelligent environments such as smart home and smart city. Ordinary people in their daily …

The power of end-to-end deep learning techniques to automatically learn latent high-level
features from raw signals has been demonstrated in numerous application fields, however …

Activity recognition is an important step towards automatically measuring the functional
health of individuals in smart home settings. Since the inherent nature of human activities is …

Long Short Term Memory (LSTM)-based structures have demonstrated their efficiency for
daily living recognition activities in smart homes by capturing the order of sensor activations …

Abstract Convolutional Neural Networks (CNN) are very useful for fully automatic extraction
of discriminative features from raw sensor data. This is an important problem in activity …

The recent enhancement of computerized electronic gadgets has led to the acceptance of
smart home sensing applications, stimulating a need for related services and products. As a …