Inertial measurement units (IMUs) have become the mainstay in human motion evaluation
outside of the laboratory; however, quantification of 3-dimensional upper limb motion using …

In this study, we review the role of wearable devices in tracking our daily locomotion. We
discuss types of wearable devices that can be used, methods for gait analyses, and multiple …

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 …

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 …

Estimating biological joint moments using wearable sensors could enable out-of-lab
biomechanical analyses and exoskeletons that assist throughout daily life. To realize these …

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 …

Deep learning biomechanical models perform optimally when trained with large datasets,
however these can be challenging to collect in gait labs, while limited augmentation …

This paper explores the use of biomechanics in identifying the mechanistic causes of
musculoskeletal tissue injury and degeneration. It appraises how biomechanics has been …