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Probability Histograms

Probability Histograms

A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.

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Related Experiment Video

Updated: Aug 11, 2025

Quantitative Static and Dynamic Assessment of Balance Control in Stroke Patients

Quantitative Static and Dynamic Assessment of Balance Control in Stroke Patients

Published on: May 17, 2020

Automatic characterization of stroke patients' posturography based on probability density analysis.

Ying Wang¹, Zhen Hu², Kai Chen¹

¹School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China.

Biomedical Engineering Online

|February 4, 2023

Summary

This summary is machine-generated.

Probability density analysis of center of pressure (COP) data offers a novel method for assessing stroke patients' balance. This technique effectively characterizes posturography, aiding in clinical evaluations of balance ability.

Keywords:

Balance ability Center of pressure Postural control Probability density Stroke patients

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Last Updated: Aug 11, 2025

Quantitative Static and Dynamic Assessment of Balance Control in Stroke Patients

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Published on: May 17, 2020

Author Spotlight: Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT) for NonInvasive Whole-Brain Imaging of Ischemic Stroke

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A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

Published on: September 16, 2017

Area of Science:

Biomechanics
Neurology
Rehabilitation Science

Background:

Balance impairment is a significant challenge for stroke survivors, impacting mobility and quality of life.
Traditional methods for assessing balance using center of pressure (COP) data have limitations in fully characterizing sway patterns.

Purpose of the Study:

To investigate the utility of probability density analysis for characterizing COP data in stroke patients.
To evaluate the effectiveness of this method in assessing balance ability compared to traditional parameters.

Main Methods:

COP data from 38 stroke patients during quiet standing (eyes open/closed) were collected using a force platform.
Analysis included conventional parameters (sway length, radius, area) and probability density parameters (projection area, skewness, kurtosis).
Statistical correlations and differences between eyes open and eyes closed conditions were examined.

Main Results:

Probability density analysis parameter, projection area (PA), showed strong correlation with traditional sway length (SL) and sway radius (SR).
Significant differences in both conventional and probability density parameters were observed between eyes open (EO) and eyes closed (EC) conditions.
Probability density function enabled categorization of sway types for clinical balance assessment.

Conclusions:

Probability density analysis provides a robust method for characterizing posturography in stroke patients.
This approach enhances the evaluation of balance ability, offering valuable insights for clinical practice and rehabilitation strategies.