Detection and Classification of Stroke Gaits by Deep Neural Networks Employing Inertial Measurement Units

Sensors

You-Chi Li¹  Chih-Jen Shih¹  Fu-Cheng Wang¹  Wei Yuan¹  Tien-Yun Kuo¹  Chin-Hsien Lin²  Szu-Fu Chen³  Ang-Chieh Lin³ 

[1] Department of Mechanical Engineering, National Taiwan University, Taipei 106, Taiwan;Department of Neurology, National Taiwan University Hospital, Taipei 100, Taiwan;Department of Physical Medicine and Rehabilitation, Cheng Hsin General Hospital, Taipei 112, Taiwan;

关键词: gait recognition;    deep learning;    neural network;    stroke gait;    IMU (inertial measurement unit);   

DOI  :  10.3390/s21051864

来源: DOAJ

【 摘 要 】

This paper develops Deep Neural Network (DNN) models that can recognize stroke gaits. Stroke patients usually suffer from partial disability and develop abnormal gaits that can vary widely and need targeted treatments. Evaluation of gait patterns is crucial for clinical experts to make decisions about the medication and rehabilitation strategies for the stroke patients. However, the evaluation is often subjective, and different clinicians might have different diagnoses of stroke gait patterns. In addition, some patients may present with mixed neurological gaits. Therefore, we apply artificial intelligence techniques to detect stroke gaits and to classify abnormal gait patterns. First, we collect clinical gait data from eight stroke patients and seven healthy subjects. We then apply these data to develop DNN models that can detect stroke gaits. Finally, we classify four common gait abnormalities seen in stroke patients. The developed models achieve an average accuracy of 99.35% in detecting the stroke gaits and an average accuracy of 97.31% in classifying the gait abnormality. Based on the results, the developed DNN models could help therapists or physicians to diagnose different abnormal gaits and to apply suitable rehabilitation strategies for stroke patients.

【 授权许可】

Unknown