A signal processing application for evaluating self-monitoring blood glucose strategies in a software agent model.

We propose the signal processing technique of calculating a cross-correlation function and an average deviation between the continuous blood glucose and the interpolation of limited blood glucose samples to evaluate blood glucose monitoring frequency in a self-aware patient software agent model. The diabetic patient software agent model [1] is a 24-h circadian, self-aware, stochastic model of a diabetic patient's blood glucose levels in a software agent environment. The purpose of this work is to apply a signal processing technique to assist patients and physicians in understanding the extent of a patient's illness using a limited number of blood glucose samples.

The self-aware diabetic patient software agent model.

This work presents a self-aware diabetic patient software agent for representing a human diabetic patient. To develop a 24h, stochastic and self-aware patient agent, we extend the original seminal work of Ackerman et al. [1] in creating a mathematical model of human blood glucose levels in three aspects.

The validity of gait variability and fractal dynamics obtained from a single, body-fixed triaxial accelerometer.

A single triaxial accelerometer has the ability to collect a large amount of continuous gait data to quantitatively assess the control of gait. Unfortunately, there is limited information on the validity of gait variability and fractal dynamics obtained from this device. The purpose of this study was to test the concurrent validity of the variability and fractal dynamic measures of gait provided by a triaxial accelerometer during a continuous 10 minute walk in older adults.

Evaluation of age-related differences in the stride-to-stride fluctuations, regularity and symmetry of gait using a waist-mounted tri-axial accelerometer.

Purpose: To compare the stride-to-stride fluctuations, regularity and symmetry of gait using a body-fixed accelerometer in a group of healthy young and healthy older adults.

Methods: Forty-one healthy young adults (24 ± 3 years) and forty-one healthy older adults (76 ± 5 years) completed a 10-min walk at a self-selected, normal walking speed while wearing a single waist-mounted tri-axial accelerometer. The following gait parameters were compared between age groups: mean step and stride time, step and stride time variability, stride time fractal scaling index and the regularity and symmetry of the acceleration pattern in the vertical, mediolateral and anteroposterior directions (unbiased autocorrelation procedure).