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Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...

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Related Experiment Video

Updated: May 7, 2026

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

Gait stability prediction through synthetic time-series and vision-based data.

Mauricio C Cordeiro¹, Ciaran O Cathain^2,3,4, Vitor B Nascimento⁵

¹Department of Engineering & Informatics, Technological University of the Shannon, Athlone, Ireland.

Frontiers in Sports and Active Living

|August 29, 2025

Summary

Synthetic data accurately replicates older adult gait parameters, enabling models trained on it to outperform real-world data models for predicting gait stability. Key predictors include step width, BMI, and fall history.

Keywords:

MediaPipe pose estimation SHAP values computer vision gait stability synthetic data

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Last Updated: May 7, 2026

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Published on: March 14, 2017

Comprehensive Understanding of Inactivity-Induced Gait Alteration in Rodents

Comprehensive Understanding of Inactivity-Induced Gait Alteration in Rodents

Published on: July 6, 2022

Area of Science:

Biomechanics
Computer Vision
Gerontology

Background:

Assessing gait stability in older adults is difficult due to limited data and complex measurements, especially in resource-limited settings.
Existing methods often require specialized equipment, hindering widespread application among vulnerable populations.

Purpose of the Study:

To investigate if synthetic data can accurately replicate gait parameters in older adults.
To evaluate the effectiveness of models trained on synthetic data for predicting the Margin of Stability (MoS).
To identify key biomechanical features influencing MoS predictions in older adults.

Main Methods:

A constraint-based synthetic data generation (SDG) methodology was employed using a public dataset of healthy older adults.
Gait analysis utilized smartphone-captured videos and the MediaPipe algorithm for landmark extraction, ensuring low-cost accessibility.
The SDG approach preserved biomechanical relationships through metadata configuration and rank correlation constraints.

Main Results:

The synthetic data achieved high fidelity (97.09%) in replicating gait parameters and maintaining biomechanical relationships.
Models trained solely on synthetic data (TSTR) demonstrated superior performance over real data-trained models (TRTR), with significant reductions in prediction errors (RMSE, MAE, MSE) and improved variance explanation (R²).
SHAP analysis confirmed that the synthetic data approach aligned feature attributions with established biomechanical principles, highlighting step width, BMI, and fall history as crucial predictors.

Conclusions:

Synthetic data generation is a viable method to overcome data scarcity in gait analysis for older adults.
Synthetic data-trained models significantly enhance the prediction accuracy of gait stability (MoS) compared to models trained on real data.
This approach, leveraging accessible computer vision technology, offers a promising pathway for improved clinical gait assessment in resource-limited environments.