Details

Autor(en) / Beteiligte

• Girase, Harshayu
• Nyayapati, Priya
• Booker, Jacqueline
• Lotz, Jeffrey C.
• Bailey, Jeannie F.
• Matthew, Robert P.

Titel

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

Ist Teil von

• Journal of biomechanics, 2021-11, Vol.128, p.110786-110786, Article 110786

Ort / Verlag

United States: Elsevier Ltd

Erscheinungsjahr

2021

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.