The interactive control of lower limb exoskeleton robots (LLERs) is important to achieve
compliance and safety. Significant challenges in the interactive control of LLERs include …

This research advances the field of human activity recognition (HAR) by developing a robust
and interpretable deep learning model using wearable sensor data. We address seven …

Lower limb motion intent recognition is a crucial aspect of wearable robot control and human–
machine collaboration. Among the various sensors used for this purpose, the …

Wearable lower-limb joint angle estimation using a reduced inertial measurement unit (IMU)
sensor set could enable quick, economical sports injury risk assessment and motion …

Wireless inertial motion capture holds promise for real-time human-machine interfaces and
home-based rehabilitation applications. However, wireless data drop can cause significant …

This article reports a unique state-feedback learning framework for the locomotion of a biped
robot over undulating surfaces. The proposed framework has two main components: 1) Gait …

Data diversity and quantity are crucial for training deep learning models. However, the
impact of dataset diversity and size on biomechanical variable estimation models has not …

In elderly monitoring or indoor intrusion detection, the recognition of human activity is a key
task. Owing to several strengths of Wi-Fi-based devices, including their non-contact and …

Human activity recognition using sensor data has become a research hotspot in the field of
ubiquitous computing and has a wide range of application scenarios in real life. Effectively …

As societies age, the issue of falls has become increasingly critical for the health and safety
of the elderly. Fall detection in the elderly has traditionally relied on supervised learning …