Traditional laboratory experiments, rehabilitation clinics, and wearable sensors offer
biomechanists a wealth of data on healthy and pathological movement. To harness the …

Human biomechanics and gait form an integral part of life. The gait analysis involves a large
number of interdependent parameters that were difficult to interpret due to a vast amount of …

Human gait provides a way of locomotion by combined efforts of the brain, nerves, and
muscles. Conventionally, the human gait has been considered subjectively through visual …

Objective The purpose of present review paper is to introduce the reader to key directions of
Machine Learning techniques on the diagnosis and predictions of knee osteoarthritis …

The increasing amount of data in biomechanics research has greatly increased the
importance of develo** advanced multivariate analysis and machine learning techniques …

Fatigue in lower extremity musculature is associated with decline in postural stability, motor
performance and alters normal walking patterns in human subjects. Automated recognition …

Gait analysis is helpful for rehabilitation, clinical diagnoses, and sporting activities. Among
the gathered signals, ground reaction forces (GRF) may be used for assisting doctors in …

Gait phase durations are important spatiotemporal parameters in different contexts such as
discrimination between healthy and pathological gait and monitoring of treatment outcomes …

Many machine learning models show black box characteristics and, therefore, a lack of
transparency, interpretability, and trustworthiness. This strongly limits their practical …

Background. The purpose of this study is to review the current literature on knee joint
biomechanical gait data analysis for knee pathology classification. The review is prefaced by …