Herramientas tecnológicas para la medición y registro de movimiento objetivo de la hiperactividad
- Sempere Tortosa, Mireia L. 1
- Fernández Carrasco, Francisco 1
- García Fernández, José Manuel 1
- Cantó Díez, Tomás J.
-
1
Universitat d'Alacant
info
ISSN: 2341-2526
Año de publicación: 2018
Volumen: 5
Número: 1
Páginas: 82-98
Tipo: Artículo
Otras publicaciones en: Revista de Discapacidad, Clínica y Neurociencias: (RDCN)
Resumen
In the absence of any condition that unambiguously determines the existence of ADHD, the diagnosis is clinical, and is determined by the observation and information provided by parents and teachers. This is highly subjective and leads to disparate results, largely due to the lack of agreement in the evaluation instruments and procedures. Thus, the inaccuracy of the diagnosis of ADHD, based on subjective criteria, together with the fact that hyperactivity is one of the main symptoms of this disorder, cause that, for more than a decade, several studies have been carried out to record objective measures of movements in the subjects. This paper reviews different technologies available in the market that allow the recording and measurement of the movement of a human body in order to facilitate obtaining an objective measurement of movement that supports the diagnosis of ADHD. Three different technologies are reviewed: video systems; motion capture systems based on sensors or markers; and depth map systems, describing the advantages and disadvantages of each one of them to support the diagnosis of ADHD.
Referencias bibliográficas
- Airò Farulla G, Pianu D, Cempini M, Cortese M, Russo LO, Indaco M, Nerino R, Chimienti A, Oddo CM, Vitiello N. Vision-Based Pose Estimation for Robot-Mediated Hand Telerehabilitation. Sensors (Basel). 2016 Feb 5;16(2):208. doi: 10.3390/s16020208.
- Alberola, P. Los criterios de diagnóstico son vagos, ¿cómo se determina que un niño es más o menos movido? Diario Información. 2015 Nov 15; [consultado 8 de enero de 2018] Disponible en: http://www.diarioinformacion.com/alicante/2015/11/08/criterios-diagnostico-son-vagosdetermina/1694260.html.
- American Psychiatric Publishing. Manual diagnóstico y estadístico de los trastornos mentales. 5ª ed. Arlington, VA
- Arns M, Gordon E. Quantitative EEG in psychiatry: Diagnostic or prognostic use? Clin Neurophysiol, 2014; 125(8): 1504-1506. doi: 10.1016/j.clinph.2014.01.014
- Artigas-Pallarés J. Comorbilidad en el trastorno por déficit de atención/hiperactividad. Rev Neurol 2003;36 (S1):68-0
- Aula Nesplora. El test más completo para la evaluación del TDAH [consultado 20 de febrero de 2018] Disponible en: http://aulanesplora.com
- Barkley R. A Cautionary Note about Using the EEG for Clinical Diagnosis of ADHD. ADHD Report, 2013; 21 (6): 15-15. doi: 10.1521/adhd.2013.21.6.15
- Barkley RA. Avances en el diagnóstico y la subclasificación del trastorno por déficit de atención / hiperactividad: qué puede pasar en el el futuro respecto al DSM-V. Rev Neurol 2009;48 (Supl. 2): S101-S106
- Bastardas J, Ortiz-Guerra J, Sánchez V, Sabaté J. Diagnóstico del TDAH. REV ESP PEDIATR 2015; 71(2): 69-74
- Behendi S, Morgan S, Fookes C. Non-Invasive Performance Measurement in Combat Sports. Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS). 2015;392: 3-10. doi: 10.1007/978-3-319-24560-7 1
- Boutsika E. Kinect in Education: A Proposal for Children with Autism. Procedia Comput Sci. 2014;27:123-129. doi: 10.1016/j.procs.2014.02.015
- Brekel. Tools for Kinect markerless motion capture. [consultado 8 de enero de 2018] Disponible en: http://www.brekel.com
- Catuhe, D. Programming with the Kinect for Windows Software. Developtment Kit. Redmond, WA: Microsoft Press; 2012
- Chang YJ, Chen SF, Huang JD. A Kinect-based system for physical rehabilitation: a pilot study for young adults with motor disabilities. Res Dev Disabil. 2011 Nov-Dec;32(6):2566-70. doi: 10.1016/j.ridd.2011.07.002.
- Chang YJ, Han WY, Tsai YC. A Kinect-based upper limb rehabilitation system to assist people with cerebral palsy. Res Dev Disabil. 2013 Nov;34(11):3654-9. doi: 10.1016/j.ridd.2013.08.021.
- Chen YC, Lee HJ, Lin KH. Measurement of body joint angles for physical therapy based on mean shift tracking using two low cost Kinect images. Conf Proc IEEE Eng Med Biol Soc. 2015 Aug;2015:703-6. doi: 10.1109/EMBC.2015.7318459.
- Climent G, Banterla F. AULA Nesplora. Evalución ecológica de los procesos atencionales. San Sebastián: Nesplora; 2011
- Creative Senz3D. [consultado 8 de enero de 2018] Disponible en: http://es.creative.com/p/webcameras/creative-senz3d
- Díaz-Orueta U, Garcia-López C, Crespo-Eguílaz N, Sánchez-Carpintero R, Climent G, Narbona J. AULA virtual reality test as an attention measure: Convergent validity with Conners’ Continuous Performance Test. Child Neuropsycho, 2014; 20(3): 328-342. doi: 10.1080/09297049.2013.792332
- Diaz-Orueta U, Iriarte Y, Climent G, Banterla F. AULA: An ecological virtual reality test with distractors for evaluating attention in children and adolescents. Virtual Reality 2012 [consultado 20 de febrero de 2018]; 5(2): 1-20. Disponible en: https://www.researchgate.net/publication/311589131_AULA_An_ecological_virtual_reality_test_w ith_distractors_for_evaluating_attention_in_children_and_adolescents
- Ding I, Chang C. An eigenspace-based method with a user adaptation scheme for human gesture recognition by using Kinect 3D data. Appl Math Model. 2015;39(19): 5769-5777. doi: 10.1016/j.apm.2014.12.054
- ElMindA – Visualizing your brain – revolutionizing treatment [consultado 19 de febrero de 2018] Disponible en: www.elminda.com
- Fernández M, Morillo M, Alonso L. Utilidad del estudio Aula Nesplora en la valoración del TDAH. XVI Reunión Anual de la Sociedad Española de Nueurología Peiátrica. Santander 2012.
- Galna B, Barry G, Jackson D, Mhiripiri D, Olivier P, Rochester L. Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease. Gait Posture. 2014 Apr;39(4):1062-8. doi: 10.1016/j.gaitpost.2014.01.008.
- García Murillo L, Cortese S, Anderson D, Di Martino A, Castellanos FX. Locomotor activity measures in the diagnosis of attention deficit hyperactivity disorder: Meta-analyses and new findings. J Neurosci Methods. 2015 Aug 30;252:14-26. doi: 10.1016/j.jneumeth.2015.03.001
- Gawrilow C, Kühnhausen J, Schmid J, Stadler G. Hyperactivity and Motoric Activity in ADHD: Characterization, Assessment, and Intervention. Front Psychiatry 2014; 5: 171. Gawrilow C, Kühnhausen J, Schmid J, Stadler G. Hyperactivity and Motoric Activity in ADHD: Characterization, Assessment, and Intervention. Front Psychiatry. 2014; 5: 171. doi:10.3389/fpsyt.2014.00171
- González-Ortega D, Díaz-Pernas FJ, Martínez-Zarzuela M, Antón-Rodríguez M. A Kinect-based system for cognitive rehabilitation exercises monitoring. Comput Methods Programs Biomed. 2014 Feb;113(2):620-31. doi: 10.1016/j.cmpb.2013.10.014. Epub 2013 Oct 31. PubMed PMID: 24263055.
- Hall CL, Valentine AZ, Groom MJ, Walker GM, Sayal K, Daley D, Hollis C. The clinical utility of the continuous performance test and objective measures of activity for diagnosing and monitoring ADHD in children: a systematic review. Eur Child Adolesc Psychiatry. 2016 Jul;25(7):677-99. doi: 10.1007/s00787-015-0798-x
- Hofmann M, Granter N, Witte K, Edelmann-Nusser J, Nowoisky C. Use of the Infrared Based Motion Capture System AS 200 in Sport Science. Eng Sport. 2006;6: 45–50. doi: 10.1007/978-0-387-46051-2_9
- Hussmann S, Ringbeck T, Hagebeuker B. A performance review of 3D ToF vision systems in comparison to stereo vision systems. In: Stereo Vision. 2008: 372. [consultado 8 de enero de 2018] Disponible en: http://cdn.intechweb.org/pdfs/5767.pdf
- iPi Soft - Markerless Motion Capture. [consultado 8 de enero de 2018] Disponible en: http://ipisoft.com
- Iriarte Y, Diaz-Orueta U, Cueto E, Irazustabarrena P, Banterla F, Climent G. AULA -advanced virtual reality tool for the assessment of attention: normative study in Spain. Journal of Attention Disorders, 2016; 20(6): 542-568. doi: 10.1177/1087054712465335
- Jaiswal S, Valstar M, Gillott A, Daley D. Automatic detection of ADHD and ASD from Expressive Behaviour in RGBD Data. CoRR. 2016. [consultado 8 de enero de 2018] Disponible en: http://www.cs.nott.ac.uk/~psxsj3/publications/fg2017.pdf
- Jana, A. Kinect for Windows SDK: programming guide. Packt Publishing; 2012.
- Kanetaka H, Yabukami S, Hashi S, Arai K. Wireless magnetic motion capture system for medical use. In T. Sasano & O. Suzuki, eds. Interface Oral Health Science. Japan: Springer; 2009. p. 329– 331.
- Kinect para Xbox One. [consultado 8 de enero de 2018] Disponible en: http://www.xbox.com/esES/Kinect
- Kynovea. [consultado 8 de enero de 2018] Disponible en: http://kynovea.org
- Lasith K, Hirakawa M. GestureTank: A gesture detection water vessel for foot movements. ICTer Journal. 2015;8(2). [consultado 8 de enero de 2018] Disponible en: http://journal.icter.org/index.php/ICTer/article/view/203
- Li F, Zheng Y, Smith SD, Shic F, Moore C, Zheng X, et al. A preliminary study of movement intensity during a Go/No-Go task and its association with ADHD outcomes and symptom severity. Child Adolesc Psychiatry Ment Health. 2016;10:47. doi:10.1186/s13034-016-0135-2
- NEBA Health. [consultado 19 de febrero de 2018] Disponible en: http://www.nebahealth.com/research.html
- Negut A, Jurma AM, David D. Virtual-reality-based attention assessment of ADHD: ClinicaVR: Classroom-CPT versus a traditional continuous performance test. Child Neuropsychol, 2017; 23(6): 692-712. doi: 10.1080/09297049.2016.1186617
- Ohashi K, Vitaliano G, Polcari A, Teicher MH. Unraveling the nature of hyperactivity in children with attention-deficit/hyperactivity disorder. Arch Gen Psychiatry. 2010 Apr;67(4):388-96. doi: 10.1001/archgenpsychiatry.2010.28
- O'Mahony N, Florentino-Liano B, Carballo JJ, Baca-García E, Rodríguez AA. Objective diagnosis of ADHD using IMUs. Med Eng Phys. 2014 Jul;36(7):922-6. doi: 10.1016/j.medengphy.2014.02.023
- Parsons TD, Bowerly T, Buckwalter J G, Rizzo AA. A controlled clinical comparison of attention performance in children with ADHD in a virtual reality classroom compared to standard neuropsychological methods. Child Neuropsychol 2014; 13(4): 363-381. doi: 10.1080/13825580600943473
- Pedraza-Hueso M, Martín-Calzón S, Díaz-Pernas F, Martínez-Zarzuela M. Rehabilitation Using Kinect-based Games and Virtual Reality. Procedia Comput Sci. 2015;75:161-168. doi: 10.1016/j.procs.2015.12.233
- PlayStation Camera - Camera for PS4 Console. [consultado 8 de enero de 2018] Disponible en: https://www.playstation.com/en-us/explore/accessories/playstation-camera-ps4/
- PMD technologies. [consultado 8 de enero de 2018] Disponible en: http://www.pmdtec.com/news_media/video/camcube.php
- Poli S, Vollmann S, Ghisleni C, O’Gorman RL, Klaver P, Ball J. Age dependent electroencephalographic changes in Attention Deficit/Hyperactivity Disorder (ADHD). Clinical Neurophysiol, 2014; 125(8): 1626-1638. doi: 10.1016/j.clinph.2013.12.118
- Qbtech: transforming healthcare. [consultado 19 de febrero de 2018] Disponible en: https://www.qbtech.com/qbtest.html
- Quotient ADHD System. [consultado 19 de febrero de 2018] Disponible en: http://quotientadhd.com
- Rodríguez C, Quintero H, Aschner H. Movimiento del brazo humano: de los tres planos a las tres dimensiones. Rev Ing. 2005;22:36-44. [consultado 8 de enero de 2018] Disponible en: http://www.redalyc.org/pdf/1210/121014219003.pdf
- Seer S, Brändle N, Ratti C. Kinects and human kinetics: A new approach for studying pedestrian behavior. Transp Res Part C Emerg Technol. 2014;48: 212 - 228. doi: http://dx.doi.org/10.1016/j.trc.2014.08.012
- Softkinetic - 3D Vision Leader. [consultado 8 de enero de 2018] Disponible en: http://www.softkinetic.com/Products/DepthSenseCameras
- Structure Sensor - 3D scanning, augmented reality, and more for mobile devices. [consultado 8 de enero de 2018] Disponible en: http://structure.io/
- Suarez J, Murphy RR. Hand gesture recognition with depth images: A review. In: IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication. Paris, France, septiembre 2012. doi: 10.1109/ROMAN.2012.6343787
- Sugiura N, Komuro T. Dynamic 3D interaction using an optical See-through HMD. Virtual Reality (VR). [consultado 8 de enero de 2018] Disponible en: http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=7223444
- SwissRanger | Heptagon. [consultado 20 de febrero de 2018] Disponible en: http://hptg.com/industrial/
- Teicher MH, Polcari A, McGreenery CE. Utility of Objective Measures of Activity and Attention in the Assessment of Therapeutic Response to Stimulants in Children with AttentionDeficit/Hyperactivity Disorder. J Child Acolesc Psychopharmacol. 2008;18(3):265-270. doi:10.1089/cap.2007.0090
- Thalmann D, Gutierrez M, Vexo F. Stepping into VirtualReality. London: Springer-Verlag; 2008
- The power of visual learning. [consultado 8 de enero de 2018] Disponible en: http://www.dartfish.com
- Tracker Video Analysis and Modeling Tool. [consultado 8 de enero de 2018] Disponible en: http://www.opensourcephysics.org/items/detail.cfm?ID=7365
- Vlasic D, Adelsberger R, Vannucci G, Barnwell J, Gross M, Matusik W, et al. Practical motion capture in everyday surroundings. ACM Trans Graph. 2007;23(7). doi: 10.1145/1276377.1276421
- Wehmeier PM, Schacht A, Wolff C, Otto WR, Dittmann RW, Banaschewski T. Neuropsychological outcomes across the day in children with attention-deficit/hyperactivity disorder treated with atomoxetine: results from a placebo-controlled study using a computer-based continuous performance test combined with an infra-red motion-tracking device. J Child Adolesc Psychopharmacol. 2011 Oct;21(5):433-44. doi: 10.1089/cap.2010.0142
- Xtion PRO | Sensores de movimiento. [consultado 8 de enero de 2018] Disponible en: https://www.asus.com/es/3D-Sensor/Xtion_PRO/
- Zalud L, Kotova M, Kocmanová P, Dobsak P, Kolarova J. Breath Analysis Using a Time-of-Flight Camera and Pressure Belts. Artif Organs. 2016 Jun;40(6):619-26. doi: 10.1111/aor.12592.
- ZED - Stereo Camera for Depth Sensing. [consultado 8 de enero de 2018] Disponible en: https://www.stereolabs.com/
- Zhou Y, Jiang G, Lin Y. A novel finger and hand pose estimation technique for real-time hand gesture recognition. Pattern Recognition. 2016;49: 102-114. doi: 10.1016/j.patcog.2015.07.014