Markerless Active Trunk Shape Modelling for Motion Tolerant Remote Respiratory Assessment

We present a vision-based trunk-motion tolerant approach which estimates lung volume-time data remotely in forced vital capacity (FVC) and slow vital capacity (SVC) spirometry tests. After temporal modelling of trunk shape, generated using two opposing Kinects in a sequence, the chest-surface respiratory pattern is computed by performing principal component analysis on temporal geometrical features extracted from the chest and posterior shapes. We evaluate our method on a publicly available dataset of 35 subjects (300 sequences) and compare against the state-of-the-art. By filtering complex trunk motions, our proposed method calibrates the entire volume-time data using only the tidal volume scaling factor which reduces the state-of-the-art average normalised L2 error from 0.136 to 0.05.