Recurrent neural network model of gait can predict Parkinson’s disease

View Full Paper

Ask AI

Bookmark

Authors

Discussion

Analysis shows recurrent neural network predicts gait alterations in Parkinson’s disease, suggesting use of wearable sensors for early detection.

The recurrent neural network revealed a 96.4% accuracy in distinguishing Parkinson’s disease patients from controls.
High-resolution gait data was collected from 166 participants using wearable sensors during a 120-second walking task.
This analysis utilized force measurements normalized to reduce individual variability before applying machine learning techniques.
The findings imply that early intervention may be supported by non-invasive detection methods for Parkinson’s disease.

RobertPatrick Selvam (2025) studied this question.

View Full Paper

Ask AI

Bookmark

View Full Paper

Ask AI

Bookmark

Authors

Analysis shows recurrent neural network predicts gait alterations in Parkinson’s disease, suggesting use of wearable sensors for early detection.

The recurrent neural network revealed a 96.4% accuracy in distinguishing Parkinson’s disease patients from controls.
High-resolution gait data was collected from 166 participants using wearable sensors during a 120-second walking task.
This analysis utilized force measurements normalized to reduce individual variability before applying machine learning techniques.
The findings imply that early intervention may be supported by non-invasive detection methods for Parkinson’s disease.

RobertPatrick Selvam (2025) studied this question.

View Full Paper

Ask AI

Bookmark