Total knee arthroplasty (TKA) is a surgical procedure used in patients with Osteoarthritis to improve their state. An understanding about how gait patterns differ from patient to patient and are influenced by the assistive device (AD) that is prescribed is still missing. This article focuses on such purpose. Standard walker, crutches and rollator were tested. Symmetric indexes of spatiotemporal and postural control features were calculated. In order to select the important features which can discriminate the differences among the ADs, different techniques for feature selection are investigated. Classification is handled by Multi-class Support Vector Machine. Results showed that rollator provides a more symmetrical gait and crutches demonstrated to be the worst. Relatively to postural control parameters, standard walker is the most stable and crutches are the worst AD. This means that, depending on the patient's problem and the recovery goal, different ADs should be used. After selecting a set of 16 important features, through correlation, it was demonstrated that they provide important quantitative information about the functional capacity, which is not represented by velocity, cadence and clinical scales. Also, they were capable of distinguishing the gait patterns influenced by each AD, showing that each patient has different needs during recovery. Implications of Rehabilitation An understanding about how gait patterns of post-surgical patients differ from person to person and how they are influenced by the type of device that is prescribed during their recovery might help in physical therapy. Research specifically addressing these issues is still missing. Inter-limb asymmetry and postural control features can be evaluated in an outpatient setting, supplying important additional information about individual gait pattern, which is not represented by gait velocity, cadence and scales usually used. The features calculated in this study are able to provide complementary information to gait velocity, cadence and clinical scales to assess the functional capacity of patients that passed through TKA. The selected parameters make a new clinical tool useful for tracking the evolution of patients' recovery after TKA.
Keywords: Assistive devices; feature reduction; gait analysis; genetic algorithms; multi-class classification.