Efficient Sitting Posture Recognition for Wheelchair Users: An Unsupervised Data-Driven Framework

Abstract

Automatic and reliable detection of a person's posture when sitting in a wheelchair is necessary to prevent major health issues. This study introduces an unsupervised anomaly detection and isolation approach to automatically recognize unbalanced sitting posture in a wheelchair using data from pressure sensors embedded in the wheelchair. Importantly, the advantages of independent component analysis (ICA) will be integrated with those of a Kantorovich Distance (KD)-driven anomaly detector by developing an ICA-driven KD methodology that can handle non-Gaussianity in the data and ameliorates the quality of anomaly detection. Due to pressure data displaying a non-Gaussian behavior, this work adopts ICA, which is well suited to handle this type of data. At the same time, the KD scheme is an effective anomaly detection indicator to evaluate the ICA residuals. Furthermore, the contribution plot strategy, which does not need a priori knowledge of anomalies, is employed for discriminating the type of the detected abnormal posture if it is caused due to higher pressure on the right side, on the left side, or higher forward pressure. The ICA-KD approach only employs normal events data to train the detection model, making them more attractive for identifying a person's posture in practice. The overall detection system provides a promising performance with an F1-score around 99.41%, outperforming some commonly used monitoring methods.