Over the past half-century, musculoskeletal simulations have deepened our knowledge of
human and animal movement. This article outlines ten steps to becoming a musculoskeletal …

Over the past few years, there has been notable advancement in the field of Quantified Gait
Analysis (QGA), thanks to machine learning techniques. QGA and gait prediction are areas …

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 …

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 …

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

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

We propose a co-simulation framework comprising biomechanical human body models and
wearable inertial sensor models to analyse gait events dynamically, depending on inertial …

The purpose of this study is to develop a wearable paradigm to accurately monitor Achilles
tendon loading and walking speed using wearable sensors that reduce subject burden. Ten …

Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning
models, demonstrate the best performance when trained using extensive datasets …

Reliable estimation of desired motion trajectories plays a crucial part in the continuous
control of lower extremity assistance devices such as prostheses and orthoses. Moreover …