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

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 …

Principal component analysis of a data matrix extracts the dominant patterns in the matrix in
terms of a complementary set of score and loading plots. It is the responsibility of the data …

Research Methods in Biomechanics, Second Edition, demonstrates the range of available
research techniques and how to best apply this knowledge to ensure valid data collection. In …

Background Finding the best subset of gait features among biomechanical variables is
considered very important because of its ability to identify relevant sports and clinical gait …

Mechanical factors have been implicated in the progression of knee osteoarthritis (OA).
Understanding how these factors change as the condition progresses would elucidate their …

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

Gait symmetry is important in measuring gait pattern alterations for establishing the level of
functional limitation due to pathology, observing its changes over time and evaluating …

Knee osteoarthritis (OA) is a multifactoral, progressive disease process of the
musculoskeletal system. Mechanical factors have been implicated in the progression of …

Background automatic recognition of human movement is an effective strategy to assess
abnormal gait patterns. Machine learning approaches are mainly applied due to their ability …