Automated assessment and classification of spine, hip, and knee pathologies from sit-to-stand movements collected in clinical practice.

Efficient, cost-effective methods for quantifying patient biomechanics at the point of care can facilitate faster and more accurate diagnoses. This work presents a new method to diagnose pre-surgical back, hip, and knee patients by analysing their sit-to-stand motion captured by a Kinect camera. Kinematic and dynamic time-series features were extracted from patient movements collected in clinic. These features were used to test a variety of machine learning methods for patient classification. The performance of models trained on time-series features were compared against models trained on domain-knowledge features, highlighting the importance of using time-series data for the classification of human movement. Additionally, the effectiveness of using semi-supervised learning is tested on partially labelled datasets, providing insight on how to boost classification performance in situations where labelled patient data is difficult to obtain. The best semi-supervised model achieves ∼73% accuracy in distinguishing individuals with low-back pain, and hip and knee degeneration from control subjects.