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Updated: Jul 5, 2025

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

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Hidden Markov Model for Parkinson's Disease Patients Using Balance Control Data.

Khaled Safi¹, Wael Hosny Fouad Aly², Hassan Kanj²

¹Computer Science Department, Jinan University, Tripoli P.O. Box 818, Lebanon.

Bioengineering (Basel, Switzerland)

|January 22, 2024

Summary

This summary is machine-generated.

A novel hidden Markov model (HMM) accurately distinguishes Parkinson's disease (PD) patients from healthy individuals using raw stabilometric data. This approach achieves up to 98% accuracy, offering a promising tool for early detection and management of PD.

Keywords:

HMM Parkinson’s disease machine learning postural stability stabilometric data

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Quantitative Static and Dynamic Assessment of Balance Control in Stroke Patients

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Published on: May 17, 2020

Area of Science:

Biomechanics
Neurology
Data Science

Background:

Human postural control is vital for balance in static and dynamic conditions.
Parkinson's disease (PD) severely impairs stability, increasing fall risk.
Objective assessment of PD-related postural deficits is crucial.

Purpose of the Study:

To develop and validate a novel method for differentiating individuals with PD from healthy controls.
To assess the efficacy of a hidden Markov model (HMM) using raw stabilometric data.

Main Methods:

Utilized a hidden Markov model (HMM) for classification.
Employed raw, preprocessed stabilometric signals from 60 participants (healthy and PD).
Evaluated the model's accuracy in distinguishing between the two groups.

Main Results:

The HMM achieved a high accuracy rate of up to 98% in differentiating healthy subjects from PD patients.
The method demonstrated effectiveness using unprocessed stabilometric data.
Successful distinction between healthy and PD groups was achieved.

Conclusions:

The proposed HMM-based approach offers a highly accurate and efficient method for identifying Parkinson's disease.
Utilizing raw stabilometric data simplifies the diagnostic process.
This technique holds potential for non-invasive PD diagnosis and monitoring.