Actigraphy-based scratch detection using logistic regression

Johanna Petersen, Daniel Austin, Robert Sack, Tamara L. Hayes

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


Incessant scratching as a result of diseases such as atopic dermatitis causes skin break down, poor sleep quality, and reduced quality of life for affected individuals. In order to develop more effective therapies, there is a need for objective measures to detect scratching. Wrist actigraphy, which detects wrist movements over time using microaccelerometers, has shown great promise in detecting scratch because it is lightweight, usable in the home environment, can record longitudinally, and does not require any wires. However, current actigraphy-based scratch-detection methods are limited in their ability to discriminate scratch from other nighttime activities. Our previous work demonstrated the separability of scratch from both walking and restless sleep using a clustering technique which employed four features derived from the actigraphic data: number of accelerations above 0.01 g's, epoch variance, peak frequency, and autocorrelation value at one lag. In this paper, we extended these results by employing these same features as independent variables in a logistic regression model. This allows us to directly estimate the conditional probability of scratching for each epoch. Our approach outperforms competing actigraphy-based approaches and has both high sensitivity (0.96) and specificity (0.92) for identifying scratch as validated on experimental data collected from 12 healthy subjects. The model must still be fully validated on clinical data, but shows promise for applications to clinical trials and longitudinal studies of scratch.

Original languageEnglish (US)
Pages (from-to)277-283
Number of pages7
JournalIEEE journal of biomedical and health informatics
Issue number2
StatePublished - 2013
Externally publishedYes


  • Atopic dermatitis
  • Generalized linear models
  • Logistic regression
  • Scratch

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics
  • Electrical and Electronic Engineering
  • Health Information Management


Dive into the research topics of 'Actigraphy-based scratch detection using logistic regression'. Together they form a unique fingerprint.

Cite this