Abstract Human Activity Recognition (HAR) plays a significant role in the everyday life of
people because of its ability to learn extensive high-level information about human activity …

The uncertainty and dynamic of home environment present great challenges to the task
planning of service robots. The nature of the home environment is highly unstructured, with a …

Smartphones and wearable devices have built-in sensors that can collect multivariant time-
series data that can be used to recognize human activities. Research on human activity …

With the increasing popularity of wearable sensors, deep-learning-based human activity
recognition (HAR) has attracted great interest from both academic and industrial fields in …

Sensor-based human activity recognition with wearable devices has captured the attention
of researchers in the last decade. The possibility of collecting large sets of data from various …

Sleep apnea syndrome (SAS) is a common chronic respiratory disorder, which seriously
harms human health. In order to realize the large-scale promotion of SAS detection, the SAS …

Deep learning (DL) for sensor-based human activity recognition (HAR) has been a focus of
research in recent years. Sensor data stream segmentation is a core element in HAR, which …

The research in human activity recognition has gained prominence in various applications,
including healthcare, medical, and surveillance. The earlier popular techniques which relied …

In recent years, sensor-based human activity recognition (HAR) technology has been the
focus of extensive research and has been successfully applied to many aspects of people's …

A significant challenge for a supervised learning approach to inertial human activity
recognition is the heterogeneity of data generated by individual users, resulting in very poor …