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Updated: Jun 19, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

A computationally advantageous system for fitting probabilistic decompression models to empirical data.

Laurens E Howle¹, Paul W Weber, Richard D Vann

¹Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708-0300, USA. laurens.howle@duke.edu

Computers in Biology and Medicine

|October 27, 2009

Summary

This summary is machine-generated.

Researchers created a system to evaluate decompression models for predicting decompression sickness (DCS) probability. This advanced system accurately predicts DCS incidents and occurrence times, improving upon existing methods.

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Area of Science:

Physiology
Biomedical Engineering
Computational Biology

Background:

Decompression sickness (DCS) poses risks in diving and aerospace.
Accurate prediction of DCS is crucial for safety.
Existing decompression models require robust evaluation methods.

Purpose of the Study:

To develop and validate a system for assessing decompression model performance.
To enhance the accuracy of predicting DCS probability and timing.
To refine computational methods for model parameter estimation.

Main Methods:

Developed a novel system for empirical data-driven evaluation of decompression models.
Employed maximum likelihood techniques for model parameter estimation.
Derived exact integrals for risk functions and tissue kinetics transition times.

Main Results:

Achieved excellent agreement with previously published results.
Maximum likelihood values were within one log-likelihood unit of prior findings.
Mean predicted DCS incidents closely matched observed DCS (within 1.4%).
Accurate prediction of DCS occurrence time was demonstrated.

Conclusions:

The developed system effectively evaluates decompression models.
The methodology improves the prediction accuracy of DCS.
Optimized computational techniques accelerated model development.