Genetic programming for knowledge extraction from star excursion balance test

Athletes are subjected to extreme physical conditions on various parts of the body depending on the nature of the sport. Timely checkup and suitable actions are essential to maintain the required level of fitness. Star Excursion Balance Test (SEBT) is a functional test to assess the dynamic balance and lower body stability of athletes. The knee condition plays a significant role on stability, and hence on the outcome of the SEBT results. The SEBT results being high dimensional, gathering information about the knee condition from it becomes very difficult for physiotherapists. Knowledge extracted from the existing data will assist the doctors to analyze the SEBT results and diagnose the athletes better. This paper presents an evolutionary approach to evolve an expression that can deduce useful information from the SEBT results. Genetic Programming (GP) is used to evolve expressions in terms of the parameters of the SEBT test. These results reduce the complexity by recognizing the critical variables and aiding the physiotherapists. Based on the expressions obtained, Cluster plot analysis is also carried out and the results are presented.