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 …

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

Deep learning models developed to predict knee joint kinematics are usually trained on
inertial measurement unit (IMU) data from healthy people and only for the activity of walking …

In the neurorehabilitation field, robot-aided motion analysis (R-AMA) could be helpful for two
main reasons:(1) it allows the registration and monitoring of patients' motion parameters in a …

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 …

Through wearable sensors and deep learning techniques, biomechanical analysis can
reach beyond the lab for clinical and sporting applications. Transformers, a class of recent …

Measurement of human body movement is an essential step in biomechanical analysis. The
current standard for human motion capture systems uses infrared cameras to track reflective …

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

Gait performance is an important marker of motor and cognitive decline in older adults. An
instrumented gait analysis resorting to inertial sensors allows the complete evaluation of …

ABSTRACT VAN HOOREN, B., L. VAN RENGS, and K. MEIJER. Predicting Musculoskeletal
Loading at Common Running Injury Locations Using Machine Learning and Instrumented …