Wrist actigraphy for scratch detection in the presence of confounding activities.

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

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Scratching is a symptom of many dermatological disorders, especially atopic dermatitis. For the development of anti-itch medications, there is a need for objective measures of scratching. Wrist actigraphy (monitoring wrist and hand movements with micro-accelerometers) is a promising method for assessing scratching; however, currently available technology has a limited capacity to discriminate scratching from other similar movements. In this study, we investigated methods to improve the specificity of actigraphy for scratch detection on movement data collected from subjects using the PAM-RL actigraph. A k-means cluster analysis was used to differentiate scratching from walking and restless sleep, which are potential confounds for nighttime scratching. Features used in the analysis include variance, peak frequency, autocorrelation value at one lag, and number of counts above 0.01 g's. The k-means cluster analysis exhibited a high sensitivity (0.90 ± 0.10) and specificity for walking (0.98 ± 0.05) and restless sleep (0.88 ± 0.06), respectively, demonstrating the separability of these activities. This work indicates that the features described here can be used to develop a classifier that discriminates scratch from other activities. The described method of scratch detection shows promise as an objective method for assessing scratching movements in clinical trials and longitudinal studies of scratch.

Fingerprint

Actigraphy
Cluster analysis
Wrist
Pulse amplitude modulation
Accelerometers
Autocorrelation
Cluster Analysis
Classifiers
Somnambulism
Monitoring
Atopic Dermatitis
Walking
Longitudinal Studies
Analysis of Variance
Sleep
Hand
Clinical Trials
Technology

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

@article{119e58a688414fb69397aa411b0ef3a6,
title = "Wrist actigraphy for scratch detection in the presence of confounding activities.",
abstract = "Scratching is a symptom of many dermatological disorders, especially atopic dermatitis. For the development of anti-itch medications, there is a need for objective measures of scratching. Wrist actigraphy (monitoring wrist and hand movements with micro-accelerometers) is a promising method for assessing scratching; however, currently available technology has a limited capacity to discriminate scratching from other similar movements. In this study, we investigated methods to improve the specificity of actigraphy for scratch detection on movement data collected from subjects using the PAM-RL actigraph. A k-means cluster analysis was used to differentiate scratching from walking and restless sleep, which are potential confounds for nighttime scratching. Features used in the analysis include variance, peak frequency, autocorrelation value at one lag, and number of counts above 0.01 g's. The k-means cluster analysis exhibited a high sensitivity (0.90 ± 0.10) and specificity for walking (0.98 ± 0.05) and restless sleep (0.88 ± 0.06), respectively, demonstrating the separability of these activities. This work indicates that the features described here can be used to develop a classifier that discriminates scratch from other activities. The described method of scratch detection shows promise as an objective method for assessing scratching movements in clinical trials and longitudinal studies of scratch.",
author = "Johanna Feuerstein and Daniel Austin and Robert Sack and Hayes, {Tamara L.}",
year = "2011",
language = "English (US)",
volume = "2011",
pages = "3652--3655",
journal = "Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference",
issn = "1557-170X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Wrist actigraphy for scratch detection in the presence of confounding activities.

AU - Feuerstein, Johanna

AU - Austin, Daniel

AU - Sack, Robert

AU - Hayes, Tamara L.

PY - 2011

Y1 - 2011

N2 - Scratching is a symptom of many dermatological disorders, especially atopic dermatitis. For the development of anti-itch medications, there is a need for objective measures of scratching. Wrist actigraphy (monitoring wrist and hand movements with micro-accelerometers) is a promising method for assessing scratching; however, currently available technology has a limited capacity to discriminate scratching from other similar movements. In this study, we investigated methods to improve the specificity of actigraphy for scratch detection on movement data collected from subjects using the PAM-RL actigraph. A k-means cluster analysis was used to differentiate scratching from walking and restless sleep, which are potential confounds for nighttime scratching. Features used in the analysis include variance, peak frequency, autocorrelation value at one lag, and number of counts above 0.01 g's. The k-means cluster analysis exhibited a high sensitivity (0.90 ± 0.10) and specificity for walking (0.98 ± 0.05) and restless sleep (0.88 ± 0.06), respectively, demonstrating the separability of these activities. This work indicates that the features described here can be used to develop a classifier that discriminates scratch from other activities. The described method of scratch detection shows promise as an objective method for assessing scratching movements in clinical trials and longitudinal studies of scratch.

AB - Scratching is a symptom of many dermatological disorders, especially atopic dermatitis. For the development of anti-itch medications, there is a need for objective measures of scratching. Wrist actigraphy (monitoring wrist and hand movements with micro-accelerometers) is a promising method for assessing scratching; however, currently available technology has a limited capacity to discriminate scratching from other similar movements. In this study, we investigated methods to improve the specificity of actigraphy for scratch detection on movement data collected from subjects using the PAM-RL actigraph. A k-means cluster analysis was used to differentiate scratching from walking and restless sleep, which are potential confounds for nighttime scratching. Features used in the analysis include variance, peak frequency, autocorrelation value at one lag, and number of counts above 0.01 g's. The k-means cluster analysis exhibited a high sensitivity (0.90 ± 0.10) and specificity for walking (0.98 ± 0.05) and restless sleep (0.88 ± 0.06), respectively, demonstrating the separability of these activities. This work indicates that the features described here can be used to develop a classifier that discriminates scratch from other activities. The described method of scratch detection shows promise as an objective method for assessing scratching movements in clinical trials and longitudinal studies of scratch.

UR - http://www.scopus.com/inward/record.url?scp=84862590601&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84862590601&partnerID=8YFLogxK

M3 - Article

C2 - 22255131

AN - SCOPUS:84862590601

VL - 2011

SP - 3652

EP - 3655

JO - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

JF - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

SN - 1557-170X

ER -