Kinematic analysis is often performed in a lab using optical cameras combined with
reflective markers. With the advent of artificial intelligence techniques such as deep neural …

Inertial motion analysis is having a growing interest during the last decades due to its
advantages over classical optical systems. The technological solution based on inertial …

A combination of wearable sensors' data and Machine Learning (ML) techniques has been
used in many studies to predict specific joint angles and moments. The aim of this study was …

The application of artificial intelligence techniques to wearable sensor data may facilitate
accurate analysis outside of controlled laboratory settings—the holy grail for gait clinicians …

Machine learning is a promising approach to evaluate human movement based on
wearable sensor data. A representative dataset for training data-driven models is crucial to …

Gait analysis based on inertial sensors has become an effective method of quantifying
movement mechanics, such as joint kinematics and kinetics. Machine learning techniques …

The ankle joint plays a crucial role in gait, facilitating the articulation of the lower limb,
maintaining foot-ground contact, balancing the body, and transmitting the center of gravity …

Human kinetics, specifically joint moments and ground reaction forces (GRFs) can provide
important clinical information and can be used to control assistive devices. Traditionally …

In biomechanics, joint kinetics has an important role in evaluating the mechanical load of the
joint and understanding its motor function. Although an optical motion capture (OMC) system …

The joint angle during gait is an important indicator, such as injury risk index, rehabilitation
status evaluation, etc. To analyze gait, inertial measurement unit (IMU) sensors have been …