Best Features Selection for the Implementation of a Postural Sway Classification Methodology on a Wearable Node

Abstract

The possibility of identifying potential altered postural status in frail people, including patients with Parkinson Disease, represents an important clinical outcome in the management of frail elderly subjects, since this could lead to greater instability and consequently an increased risk of falling. Several solutions proposed in the literature for the monitoring of the postural behavior use infrastructure-dependent approaches or wearable devices, which do not allow to distinguish among different kinds of postural sways. In this paper a low cost and effective wearable solution to classify four different classes of postural behaviors (Standing, Antero-Posterior, Medio-Lateral and Unstable) is proposed. The solution exploits a sensor node, equipped by a tri-axial accelerometer, and a dedicated algorithm implementing the classification task. Different quantities are proposed to assess performance of the proposed strategy, with particular regards to the system capability to correctly classify an unknown pattern, through the index Q%, and the Reliability Index, RI%. Results achieved across a wide dataset demonstrated the suitability of the methodology developed, with Q%=99.84% and around 70% of classifications showing a RI% above 65%.