Recognizing the feature of knee hyperextension in stroke patients based on transformer-long-short-term memory model

Recognizing knee hyperextension during gait in stroke patients is clinically challenging and involves cumbersome, costly procedures. This study proposes a simplified strategy based on a transformer-long-short-term memory (transformer-LSTM) approach for knee hyperextension recognition via surface electromyography (sEMG) data.

This study proposes an algorithmic model to streamline knee hyperextension screening in stroke patients and reduce associated screening costs.

Using an open-source gait database of 50 stroke patients, co-contraction index (CCI) values-derived from Biceps Femoris (BF)/Rectus Femoris (RF), Tibialis Anterior (TA)/Gastrocnemius (GAS), and their combinations-were input into the model. Performance was evaluated using accuracy, F1 scores, Receiver Operating Characteristic (ROC) curves, and loss function convergence.

The transformer-LSTM achieved a recognition accuracy of 83.38% (F1: 0.8335), outperforming linear regression (36.78%, F1: 0.3405), support vector machines (38.65%, F1: 0.3185), convolutional neural networks (72.56%, F1: 0.7259), gated recurrent units (70.94%, F1: 0.7103), long-short-term memory (76.86%, F1: 0.7687), and transformer (75.76%, F1: 0.7678). The transformer-LSTM demonstrated the fastest loss function convergence and an ROC Area Under Curve of 0.99.

The transformer-LSTM provides superior recognition rates (83.38%) and offers a robust solution for screening knee hyperextension in stroke patients, highlighting its potential for clinical application.