Classification of activity engagement in individuals with severe physical disabilities using signals of the peripheral nervous system

PLoS ONE

Volumn 7, Issue 2, 2012, Pages

  Kushki, Azadeh ^a   Andrews, Alexander J ^a   Power, Sarah D ^a,b   King, Gillian ^a   Chau, Tom ^a,b  

^a University of Toronto   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ADULT; AROUSAL; ARTICLE; BRAIN ELECTROPHYSIOLOGY; CLINICAL ARTICLE; CONTROLLED STUDY; DISEASE SEVERITY; FEMALE; HUMAN; MALE; PATIENT PARTICIPATION; PATTERN RECOGNITION; PERIPHERAL NERVOUS SYSTEM; PHYSICAL ACTIVITY; PHYSICAL DISABILITY; PHYSIOLOGICAL PROCESS; PROCESS DEVELOPMENT; PSYCHOLOGIC ASSESSMENT; PSYCHOMOTOR ACTIVITY; PSYCHOSOCIAL DEVELOPMENT; SIGNAL TRANSDUCTION; TASK PERFORMANCE; ADOLESCENT; ALGORITHM; CONFIDENCE INTERVAL; DISABLED PERSON; INSTRUMENTATION; MONITORING; MOTOR ACTIVITY; PATHOPHYSIOLOGY; PHYSIOLOGY; STATISTICS;

ADOLESCENT; ALGORITHMS; CONFIDENCE INTERVALS; DISABLED PERSONS; FEMALE; HUMANS; MALE; MONITORING, PHYSIOLOGIC; MOTOR ACTIVITY; PERIPHERAL NERVOUS SYSTEM; STATISTICS AS TOPIC;

EID: 84857152856     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0030373     Document Type: Article

References (42)

1. Beukelman D, Mirenda P, (2005) Augmentative and Alternative Communication Paul H. Brookes Publishing Co.
2. Tai K, Blain S, Chau T, (2008) A review of emerging access technologies for individuals with severe motor impairments. Assist Technol 20: 204-219.
3. Wolpaw J, Birbaumer N, Heetderks W, McFarland D, Peckham P, et al. (2000) Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng 8: 164-173.
4. Donchin E, Spencer K, Wijesinghe R, (2000) The mental prosthesis: Assessing the speed of a P300-based brain-computer interface. IEEE Trans Rehabil Eng 8: 174-179.
5. Wolpaw J, McFarland D, (2004) Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc Natl Acad Sci USA 101: 17849.
6. Power S, Falk T, Chau T, (2010) Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain near-infrared spectroscopy. J Neural Eng 7: 026002.
7. Myrden A, Kushki A, Sejdic E, Guerguerian A-M, Chau T, (2011) A brain-computer interface based on bilateral transcranial doppler ultrasound. PLoS ONE 6 (9): e24170.
8. Moghimi S, Kushki A, Guerguerian AM, Chau T, (2011) EEG-Based Brain-Computer Interfaces as Access Pathways for Individuals with Severe Disabilities: A Literature Review. J Assist Technol (submitted).
9. Lund S, Light J, (2007) Long-term outcomes for individuals who use augmentative and alternative communication: Part II-communicative interaction. Augment Altern Commun 23: 1-15.
10. Light J, Drager K, (2007) AAC technologies for young children with complex communication needs: State of the science and future research directions. Augment Altern Commun 23: 204-216.
11. Gibson B, Young N, Upshur R, McKeever P, (2007) Men on the margin: A Bourdieusian examina- tion of living into adulthood with muscular dystrophy. Soc Sci Med 65: 505-517.
12. King G, Law M, King S, Rosenbaum P, Kertoy M, et al. (2003) A conceptual model of the factors affecting the recreation and leisure participation of children with disabilities. Phys Occup Ther Pediatr 23: 63-90.
13. Reeve J, Jang H, Carrell D, Jeon S, Barch J, (2004) Enhancing students' engagement by increasing teachers' autonomy support. Motiv emotion 28: 147-169.
14. Appleton J, Christenson S, Kim D, Reschly A, (2006) Measuring cognitive and psychological engagement: Validation of the student engagement instrument. J School Psychol 44: 427-445.
15. Ruble L, Robson D, (2007) Individual and environmental determinants of engagement in autism. Journal of autism and developmental disorders 37: 1457-1468.
16. McWilliam R, Bailey D, (1995) Effects of classroom social structure and disability on engagement. Topics in Early Childhood Special Education 15: 123-147.
17. Carini R, Kuh G, Klein S, (2006) Student engagement and student learning: Testing the linkages. Research in Higher Education 47: 1-32.
18. Blackstone S, Wilkins D, (2009) Exploring the importance of emotional competence in children with complex communication needs. Perspectives on Augmentative and Alternative Communication 18: 78.
19. Kreibig S, Wilhelm F, Roth W, Gross J, (2007) Cardiovascular, electrodermal, and respiratory response patterns to fear-and sadness-inducing films. Psychophysiology 44: 787-806.
20. Ekman P, Levenson R, Friesen W, (1983) Autonomic nervous system activity distinguishes among emotions. Science 221: 1208-1210.
21. Picard R, (2003) Affective computing: challenges. Int J Hum Comput Stud 59: 55-64.
22. Gazzaniga M, Ivry R, Mangun G, (2002) Cognitive neuroscience: The biology of the mind New York WW Norton & Company.
23. Levenson R, (1988) Emotion and the autonomic nervous system: A prospectus for research on autonomic specificity. Social psychophysiology and emotion: Theory and clinical applications London Wiley pp. 17-42.
24. Blain S, Chau T, Mihailidis A, (2008) Peripheral autonomic signals as access pathways for individuals with severe disabilities: a literature appraisal. The Open Rehabilitation Journal 1: 27-37.
25. Cacioppo J, Berntson G, Larsen J, Poehlmann K, Ito T, et al. (1993) The psychophysiology of emotion. Handbook of emotions New York The Guilford Press pp. 119-142.
26. Mauri M, Magagnin V, Cipresso P, Mainardi L, Brown E, et al. (2010) Psychophysiological signals associated with affective states. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) pp. 3563-3566.
27. Haag A, Goronzy S, Schaich P, Williams J, (2004) Emotion recognition using bio-sensors: First steps toward and automatic system. In: Andre E, Dybkjaer L, Minker W, Heisterkamp P, editors. pp. 36-48 Lecture Notes in Computer Science: Affective Dialogue Systems, Springer Berlin/Heidelberg, volume 3068/2004.
28. Kingsnorth S, Blain S, McKeever P, (2011) Physiological and Emotional Responses of Disabled Children to Therapeutic Clowns: A Pilot Study. Evid Based Complement Alternat Med Epub ahead of print.
29. Dawson M, Schell A, Filion D, (2000) The electrodermal system. In: Cacioppo J, Tassinary LG, G BG, editors. Handbook of psychophysiology Cambridge University Press pp. 200-223.
30. Poh M, Swenson N, Picard R, (2010) A wearable sensor for unobtrusive, long-term assessment of electrodermal activity. IEEE Trans Biomed Eng 57: 1243-1252.
31. Kistler A, Mariauzouls C, von Berlepsch K, (1998) Fingertip temperature as an indicator for sym- pathetic responses. Int J Psychophysiol 29: 35-41.
32. Leary M, Hill D, (1996) Moving on: Autism and Movement Disturbance. Ment Retard 34: 39-53.
33. McCarthy A, Blain S, Chau T, (2011) An integrated approach to detecting communicative intent amid hyperkinetic movements in children. Augment Altern Commun In press.
34. Kushki A, Fairley J, Merja S, King G, Chau T, (2011) Comparison of blood volume pulse and skin conductance responses to mental and affective stimuli at different anatomical sites. Physiol Meas In press.
35. Allen J, (2007) Photoplethysmography and its application in clinical physiological measurement. Physiol Meas 28: 1-39.
36. Lang P, Bradley M, Cuthbert B, (1997) International affective picture system (IAPS): Technical manual and affective ratings. NIMH Center for the Study of Emotion and Attention.
37. Ito T, Cacioppo J, Lang P, (1998) Eliciting affect using the international affective picture system: Trajectories through evaluative space. Pers Soc Psychol Bull 24: 855-879.
38. Ryan S, Redekop S, Barber A, (2066) Toward understanding the opinions of school-aged children about adaptive school chairs. pp. 104-107 In: Syllabus of the 22nd International Seating Symposium.
39. Schmidt S, Walach H, (2000) Electrodermal activity (EDA): State-of-the-art measurement and techniques for parapsychological purposes. J Parapsychol 63: 221-221.
40. Picard R, Vyzas E, Healey J, (2001) Toward machine emotional intelligence: Analysis of affective physiological state. IEEE Trans Pattern Anal Mach Intell 23: 1175-1191.
41. Duda R, Hart P, Stork D, (2001) Pattern classification Wiley-Interscience.
42. MÄuller-Putz G, Scherer R, Brunner C, Leeb R, Pfurtscheller G, (2008) Better than random? a closer look on bci results. International Journal of Bioelectromagnetism 10: 52-55.