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 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 …

Advances in smart home technology and IoT devices had made us capable of monitoring
human activities in a non-intrusive way. This data, in turn, enables us to predict the health …

Multi-modal data extracted from different sensors in a smart home can be fused to build
models that recognize the daily living activities of residents. This paper proposes a Deep …

Emergence of smart appliances and high performance IoT devices is promoting studies on
more functional and intelligent home services using these devices. Especially, in developed …

In recent years, Edge computing has emerged as a new paradigm that can reduce
communication delays over the Internet by moving computation power from far-end cloud …

In the era of Internet of Things (IoT), human activity recognition is becoming the vital
underpinning for a myriad of emerging applications in smart home and smart buildings …

Advances in smart home technology and IoT devices has enabled us for monitoring of
human activities for their health status and efficient energy consumption. Machine learning …

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 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 …