Related Experiment Video
Updated: Aug 29, 2025

Quantitative Static and Dynamic Assessment of Balance Control in Stroke Patients
Published on: May 17, 2020
Identification of balance recovery patterns after slips using hierarchical cluster analysis
Xinyao Hu1, Yuting Li1, Gengshu Chen1
1Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, China.
Abstract:
Accidental falls often result from loss of balance initiated by slips. People may adopt different balance recovery patterns after slips which could affect recovery outcomes. The present study aimed to identify balance recovery patterns after slips and to determine whether these balance recovery patterns could be associated with different levels of slip-induced fall likelihood. Sixty young (age 24.2 ± 2.1 years) participants were involved in an experimental study. They were instructed to walk on a linear walkway, where unexpected slips were induced when stepping onto a removable vinyl tile sheet covered with water-detergent mixture. One hundred and fifty slip trials were obtained, including 85 successful balance recovery trials and 65 failed balance recovery trials (i.e., fall trials). Hierarchical cluster analysis was used to classify balance recovery patterns based on the kinematic measures of both feet over the period from 100 to 300 ms after heel contact of the slipping foot. Three balance recovery patterns were identified, and these balance recovery patterns were found to be associated with different levels of slip-induced fall likelihood. Findings from the present study can contribute to better understanding of balance recovery mechanisms associated with slips, and guide developing and evaluating fall prevention interventions.
More Related Videos
12:27Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
07:05Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
Published on: October 27, 2016