Intelligent sitting postural anomaly detection system for wheelchair users with unsupervised techniques

Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status. To date, this detection has relied on in-person observation by medical specialists. However, given the challenges faced by health specialists to carry out continuous monitoring, the development of an intelligent anomaly detection system is proposed. Unlike other authors, where they use supervised techniques, this work proposes using unsupervised techniques due to the advantages they offer. These advantages include the lack of prior labeling of data, and the detection of anomalies previously not contemplated, among others. In the present work, an individualized methodology consisting of two phases is developed: characterizing the normal sitting pattern and determining abnormal samples. An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis. It can be concluded, among other aspects, that the utilization of dimensionality reduction techniques leads to improved results. Moreover, the normality characterization phase is deemed necessary for enhancing the system's learning capabilities. Additionally, employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.