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Measuring the Switch Cost of Smartphone Use While Walking

Measuring the Switch Cost of Smartphone Use While Walking

Published on: April 30, 2020

A method for calculating the probability of tripping while walking.

Russell Best¹, Rezaul Begg

¹Centre for Aging, Rehabilitation, Exercise & Sport and School of Human Movement, Recreation & Performance, Victoria University, Melbourne, Australia. russell.best@vu.edu.au

Journal of Biomechanics

|February 8, 2008

Summary

This summary is machine-generated.

Researchers developed a statistical model using minimum toe clearance (MTC) data to predict tripping probability. This method quantifies the risk of foot contact with obstacles during walking, aiding in falls prediction and prevention.

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Area of Science:

Biomechanics and Gait Analysis
Statistical Modeling in Health Sciences
Fall Prevention Research

Background:

Tripping is a common cause of falls, yet objective methods to quantify tripping risk are lacking.
Understanding the probability of foot-obstacle contact during gait is crucial for developing effective fall prevention strategies.

Purpose of the Study:

To introduce a novel statistical modeling technique for estimating the probability of tripping.
To quantify the risk of toe-to-obstacle contact during walking based on minimum toe clearance (MTC) variability.

Main Methods:

Collected minimum toe clearance (MTC) data from treadmill walking using a 2D geometric model.
Modeled the MTC sample distribution, incorporating skewness and kurtosis, to calculate the probability of tripping (PT).
Analyzed MTC data from a young male subject walking for one hour.

Main Results:

The MTC distribution was found to be non-normally distributed (mean=1.03 cm, S.D.=0.25 cm, skew=1.01, kurtosis=3.47).
The calculated probability of tripping (PT) for a 0.2 cm obstacle was 1 in 10,363 strides when considering skew and kurtosis.
PT increased significantly with obstacle height, highlighting the importance of accurate distribution modeling for risk assessment.

Conclusions:

Variability in MTC data can be effectively modeled to quantify the risk of tripping on unseen obstacles.
The developed method offers a potential tool for objective falls prediction and the development of targeted fall minimization interventions.