Document Type : Original Article

Authors

• Armin Salimi-Badr
• Mohammad Hashemi

Faculty of Computer Science and Engineering, Shahid Beheshti University

Abstract

In this paper, a neural approach based on Long Short-Term Memory (LSTM) neural networks is proposed to diagnose patients suffering from Parkinson’s Disease (PD). Considering the movement disorders caused by PD, the proposed method investigates the gait cycle pattern of subjects based on vertical Ground Reaction Force (vGRF) measured by 16 wearable sensors placed in subjects' shoes. In this study, it is shown that the temporal patterns of the gait cycle are different for healthy persons and patients. Therefore, by using a recurrent structure like LSTM, able to analyze the dynamic nature of the gait cycle, the proposed method extracts the temporal patterns to diagnose patients from healthy persons. To reduce the number of data dimensions, the sequences of corresponding sensors measuring vGRF in different feet are combined by subtraction. This method analyzes the temporal pattern of time series collected from different sensors, without extracting special features representing statistics of different parts of the gait cycle. Indeed, the method can extract temporal features based on learning, without using expert knowledge. Finally, the Accuracy and F1 Score of the model trained with all data is $99.87\%$, and $96.66\%$ respectively.

Keywords

• Parkinson's disease
• gait cycle
• Long-Short Term Memory

Main Subjects

• Artificial Intelligence, Robotics, Cognitive Computing