Parkinson's Diagnosis Based on Gait Patterns Using Artificial Intelligence

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Zeinaddini Meymand Farzin; Sharbatdar Mahkame

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: MODARES MECHANICAL ENGINEERING Year:2025 | Volume:25 | Issue:6 Page(s): 359-371

Download Full-Text

Persian Verion

View:

Download:

Cites:

Information Journal Paper

Title

Parkinson's Diagnosis Based on Gait Patterns Using Artificial Intelligence

Author(s)

Zeinaddini Meymand Farzin | Sharbatdar Mahkame | Issue Writer Certificate

Keywords

Parkinson

Gait Signal

Neural Network

Disease Diagnosis

Abstract

Parkinson’s disease is recognized as a progressive neurological disorder that leads to motor impairments. Due to the necessity of early and accurate diagnosis for effective disease management, a novel approach has been proposed for detecting and predicting the progression stages of Parkinson’s disease. In this study, gait signals were processed using Empirical Mode Decomposition (EMD), and temporal features were extracted by employing a hybrid CNN-LSTM deep neural network architecture. The gait data were collected using sixteen force sensors placed beneath the left and right feet of 93 individuals with Parkinson’s disease and 73 healthy controls. The signals were then preprocessed, and their intrinsic frequency components were extracted.To evaluate model performance, two training strategies were applied: a conventional train-test split, and K-Fold cross-validation. Temporal dynamics associated with disease progression were effectively extracted through the CNN-LSTM model.According to the results, the cross-validation-based model demonstrated superior accuracy of 96.44%, compared to 84.27% achieved by the simple split approach. These findings indicate that the proposed method can be reliably utilized as an intelligent, non-invasive clinical decision-support tool for the diagnosis and staging of Parkinson’s disease

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans