Identification of fall-risk factor degradations using quality of balance measurements
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Falls concern a third of the people aged over 65y and lead to the loss of functional ability. The detection of risks factors of falls is essential for early interven- tion. Six intrinsic risk factors of fall: vision, vestibular system, joint range of motion, leg muscle strength, joint proprioception and foot cutaneous propriocep- tion were assessed with clinical tests before and after temporarily degradation. Standing balance was recorded on a force plate. From the force plate, 198 parameters of the centre of pressure displacement were computed. The parame- ters were used as variables to build neural network and logistic regression model for discriminating conditions. Feature selection analysis was per- formed to reduce the number of variables. Several models were built including 3 to 10 condi- tions. Models with 5 or less conditions appeared acceptable but better performance was found with models including 3 conditions. The best accuracy was 92% for a model including ankle range of motion, fatigue and vision contrast conditions. Qualities of balance parameters were able to diag- nose impairments. However, the efficient models included only a few conditions. Models with more conditions could be built but would require a larger number of cases to reach high accuracy. The study showed that a neural network or a logistic model could be used for the diagnosis of balance impairments. Such a tool could seriously improve the prevention and rehabilitation practice.