Blanca Florentino-Liano
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Blanca Florentino-Liano has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Antonio Artés-Rodríguez
Signal Theory and Communications Department, Universidad Carlos III de Madrid, Leganés, Spain
1 shared publication - Enrique Baca-García
Department of Psychiatry, University Hospital Jimenez Diaz Foundation, Madrid, Spain
1 shared publication - Juan Jose Carballo
Puerta de Hierro Hospital, Universidad Autonoma, Madrid, Spain
1 shared publication - Niamh O'MAhoney
Dell EMC Research Europe, Cork, Ireland
1 shared publication
Latest Publications
- 20 March 2014
- Published by IOP Publishing in Medical Engineering & Physics
- Vol. 36 (7) , 922-926
Objective diagnosis of ADHD using IMUs
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".
- 20 March 2014
- Published by IOP Publishing in Medical Engineering & Physics
- Vol. 36 (7) , 922-926
Objective diagnosis of ADHD using IMUs
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".