Continuous monitoring of sitting posture in wheelchair users can enable early detection of health-related changes in functional status. Traditional observation methods rely on intermittent clinical assessments, which limit timely interventions. This paper presents a novel unsupervised anomaly detection system using pressure, inertial, or related sensor data to automatically identify deviations from personalized sitting patterns, without the need for labelled data. The method operates in two stages: (1) modeling normal posture behavior per user, and (2) detecting anomalies in real time. Comparative analysis across multiple unsupervised algorithms shows that dimensionality reduction techniques notably enhance detection accuracy. The personalization of normal posture models further improves system performance, making this an effective solution for real-time posture monitoring in wheelchair users.