2014
O'Mahony, Niamh; Florentino-Liaño, Blanca; Carballo, Juan J; Baca-García, Enrique; Artés-Rodríguez, Antonio
Objective diagnosis of ADHD using IMUs Artículo de revista
En: Medical engineering & physics, vol. 36, no 7, pp. 922–6, 2014, ISSN: 1873-4030.
Resumen | Enlaces | BibTeX | Etiquetas: Attention deficit/hyperactivity disorder, Classification, Inertial sensors, Machine learning, Objective diagnosis
@article{O'Mahony2014,
title = {Objective diagnosis of ADHD using IMUs},
author = {Niamh O'Mahony and Blanca Florentino-Lia\~{n}o and Juan J Carballo and Enrique Baca-Garc\'{i}a and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://www.tsc.uc3m.es/~antonio/papers/P50_2014_Objective Diagnosis of ADHD Using IMUs.pdf
http://www.sciencedirect.com/science/article/pii/S1350453314000459},
issn = {1873-4030},
year = {2014},
date = {2014-01-01},
journal = {Medical engineering \& physics},
volume = {36},
number = {7},
pages = {922--6},
abstract = {This work proposes the use of miniature wireless inertial sensors as an objective tool for the diagnosis of ADHD. The sensors, consisting of both accelerometers and gyroscopes to measure linear and rotational movement, respectively, are used to characterize the motion of subjects in the setting of a psychiatric consultancy. A support vector machine is used to classify a group of subjects as either ADHD or non-ADHD and a classification accuracy of greater than 95% has been achieved. Separate analyses of the motion data recorded during various activities throughout the visit to the psychiatric consultancy show that motion recorded during a continuous performance test (a forced concentration task) provides a better classification performance than that recorded during "free time".},
keywords = {Attention deficit/hyperactivity disorder, Classification, Inertial sensors, Machine learning, Objective diagnosis},
pubstate = {published},
tppubtype = {article}
}
This work proposes the use of miniature wireless inertial sensors as an objective tool for the diagnosis of ADHD. The sensors, consisting of both accelerometers and gyroscopes to measure linear and rotational movement, respectively, are used to characterize the motion of subjects in the setting of a psychiatric consultancy. A support vector machine is used to classify a group of subjects as either ADHD or non-ADHD and a classification accuracy of greater than 95% has been achieved. Separate analyses of the motion data recorded during various activities throughout the visit to the psychiatric consultancy show that motion recorded during a continuous performance test (a forced concentration task) provides a better classification performance than that recorded during "free time".