After-Stroke Arm Paresis Detection using Kinematic Data
Kenneth Lai Mohammed Almekhlafi Svetlana Yanushkevich
This paper presents an approach for detecting unilateral arm
paralysis/weakness using kinematic data. Our method employs temporal
convolution networks and recurrent neural networks, guided by knowledge
distillation, where we use inertial measurement units attached to the body to
capture kinematic information such as acceleration, rotation, and flexion of
body joints during an action. This information is then analyzed to recognize
body actions and patterns. Our proposed network achieves a high paretic
detection accuracy of 97.99\%, with an action classification accuracy of
77.69\%, through knowledge sharing. Furthermore, by incorporating causal
reasoning, we can gain additional insights into the patient's condition, such
as their Fugl-Meyer assessment score or impairment level based on the machine
learning result. Overall, our approach demonstrates the potential of using
kinematic data and machine learning for detecting arm paralysis/weakness. The
results suggest that our method could be a useful tool for clinicians and
healthcare professionals working with patients with this condition.