Diseases and injuries of the musculoskeletal locomotor system, as well as neurological disorders, can lead to pathological impairments of the human motor function. To better understand gait impairments resulting from musculoskeletal or neurological disorders, it is important for clinicians and therapists to describe and analyse the patient’s gait pattern accurately. For this purpose, gait analysis has become a crucial assessment tool. Currently, the clinical three-dimensional gait analysis (3DGA) represents the “gold standard” in clinical gait analysis.

3D gait and motion analysis to support diagnosis and decision making

3DGA is used to quantify the mechanical processes of a patient's locomotor system from a kinematic and kinetic point of view. In everyday clinical gait analysis practice, clinicians and therapists examine a large number of patients. Often, specific cases turn out to be very similar to previously examined and treated cases. Information about the course of therapy and the associated treatment outcomes of historical cases could support clinicians and therapists during the examination of new patients. However, it is difficult to manually examine the databases where medical history, 3DGA data, and information about treatment outcomes are stored in their entirety and the search for similar reference data is usually not possible. This means a vast amount of valuable clinical knowledge is currently not fully exploited.

 Advanced analysis methods 

In the project an interdisciplinary team of researchers from computer science, physiotherapy, and biomechanics will join forces to establish advanced analysis methods for modelling, classification and similarity retrieval of gait patterns. Automatic analysis methods bear the potential to provide a novel, efficient and objective way of accessing and making use of medical databases. The primary aim of this project is to apply data mining and machine learning to measurement data derived from clinical 3DGA to support clinical practice in gait analysis and decision-making. The main objectives are designing automatic classification algorithms that can robustly differentiate between a large range of gait patterns, e.g., different pathological patterns and healthy gait and developing retrieval methods for the detection of similar gait behaviour and associated diagnoses from large-scale clinical 3DGA databases. The developed methods will enable novel data-driven ways to access 3D gait databases and should lay the foundations for future clinical decision-support systems.

Information about the preceding project "IntelliGait" can be found here.