A novel implementation of sleeping posture classification using RANC ecosystem

Abstract

Sleeping posture recognition plays a vital role in various clinical applications. Many studies show that pressure sensor-based solutions work well for assessing in-bed positions. In recent years, Neuromorphic Computing has attracted many researchers' attention due to its advantage of energy efficiency. Surprisingly, the applications of Neuromorphic Computing in sleeping posture classification have been still lacking. This study proposed a novel approach that combines a preprocessing technique and an ensemble model based on a neuromorphic computing architecture called RANC. Experimental results confirm that our proposed method can gain 99.99% and 92.4% accuracy in the Leave-One-Subject-Out (LOSO) validation for 3 and 17 sleeping postures, respectively. This result greatly surpasses the previous SNN-based sleeping posture classification method.