Human activity recognition (HAR) has become increasingly popular in recent years due to its
potential to meet the growing needs of various industries. Electromyography (EMG) is …

Currently, the utilization of micro-computer-aided technologies for the dynamic motion
capture of sports digitalization is increasing gradually. To obtain the accurate athletics digital …

Various sensing modalities, including external and internal sensors, have been employed in
research on human activity recognition (HAR). Among these, internal sensors, particularly …

Sensor-based datasets are extensively utilized in human-computer interaction (HCI) and
medical applications due to their portability and strong privacy features. Many researchers …

Fatigue plays a critical role in sports science, significantly affecting recovery, training
effectiveness, and overall athletic performance. Understanding and predicting fatigue is …

Many tasks performed by two humans require mutual interaction between arms such as
handing-over tools and objects. In order for a robotic arm to interact with a human in the …

Guide dogs play a crucial role in enhancing independence and mobility for people with
visual impairment, offering invaluable assistance in navigating daily tasks and …

Traditional deep learning methods struggle to simultaneously segment, recognize, and
forecast human activities from sensor data. This limits their usefulness in many fields such as …

The prediction of human activities has garnered significant attention, owing to their
relevance in diverse applications spanning healthcare, robotics, and user-computer …

Time-series generative adversarial networks (TimeGAN) were recently developed to
produce synthetic time-series data for varied applications. Most prior uses of TimeGAN in …