Abstract
Cognitive robots constitute a highly interdisciplinary approach to the issue of therapy of children with developmental disorders. Cognitive robots become more popular, especially in action and language integration areas, joining the experience of psychologists, neuroscientists, philosophers, and even engineers. The concept of a robot as a cognitive companion for humans may be very useful. The interaction between humans and cognitive robots may be a mediator of movement patterns, learning behaviors from demonstrations, group activities, and social behaviors, as far as higher-order concepts such as symbol manipulation capabilities, words acquisition, and sensorimotor knowledge organization. Moreover there is an occupation to check many theories, such as transferring the knowledge and skills between humans and robots. Although several robotic solutions for children have been proposed the diffusion of aforementioned ideas is still limited. The review summarizes the current and future role of cognitive robots in the development and rehabilitation of children with developmental disorders.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: This work was conducted as a part of work within a project “NeuroPerCog: development of phonematic hearing and working memory in infants and children” (head: Prof. Włodzisław Duch). The project is funded by the Polish National Science Centre (DEC-2013/08/W/HS6/00333, Symfonia 1).
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
References
1. Bassolino M, Sandini G, Pozzo T. Activating the motor system through action observation: is this an efficient approach in adults and children? Dev Med Child Neurol 2015;57(Suppl 2):42–5.10.1111/dmcn.12686Search in Google Scholar PubMed
2. Ritter H, Haschke R. Hands, dexterity, and the brain. In: Cheng G, editor. Humanoid robotics and neuroscience: science, engineering and society. Boca Raton: CRC Press, 2015.Search in Google Scholar
3. Gori M, Giuliana L, Sandini G, Burr D. Visual size perception and haptic calibration during development. Dev Sci 2012;15:854–62.10.1111/j.1467-7687.2012.2012.01183.xSearch in Google Scholar PubMed
4. Gori M, Sandini G, Martinoli C, Burr D. Poor haptic orientation discrimination in nonsighted children may reflect disruption of cross-sensory calibration. Curr Biol 2010;20:223–5.10.1016/j.cub.2009.11.069Search in Google Scholar PubMed
5. De La Cruz VM, Di Nuovo A, Di Nuovo S, Cangelosi A. Making fingers and words count in a cognitive robot. Front Behav Neurosci 2014;8:13.10.3389/fnbeh.2014.00013Search in Google Scholar PubMed PubMed Central
6. Henrichs I, Elsner C, Elsner B, Wilkinson N, Gredebäck G. Goal certainty modulates infants’ goal-directed gaze shifts. Dev Psychol 2014;50:100–7.10.1037/a0032664Search in Google Scholar PubMed
7. Vercillo T, Burr D, Sandini G, Gori M. Children do not recalibrate motor-sensory temporal order after exposure to delayed sensory feedback. Dev Sci 2015;18:703–12.10.1111/desc.12247Search in Google Scholar PubMed PubMed Central
8. Gori M, Vercillo T, Sandini G, Burr D. Tactile feedback improves auditory spatial localization. Front Psychol 2014;5:1121.10.3389/fpsyg.2014.01121Search in Google Scholar PubMed PubMed Central
9. Gori M, Squeri V, Sciutti A, Masia L, Sandini G, Konczak J. Motor commands in children interfere with their haptic perception of objects. Exp Brain Res 2012;223:149–57.10.1007/s00221-012-3248-8Search in Google Scholar PubMed
10. Sciutti A, Burr D, Saracco A, Sandini G, Gori M. Development of context dependency in human space perception. Exp Brain Res 2014;232:3965–76.10.1007/s00221-014-4021-ySearch in Google Scholar PubMed
11. Daee P, Mirian MS, Ahmadabadi MN. Reward maximization justifies the transition from sensory selection at childhood to sensory integration at adulthood. PLoS One 2014;9:e103143.10.1371/journal.pone.0103143Search in Google Scholar PubMed PubMed Central
12. Gori M, Tinelli F, Sandini G, Cioni G, Burr D. Impaired visual size-discrimination in children with movement disorders. Neuropsychologia 2012;50:1838–43.10.1016/j.neuropsychologia.2012.04.009Search in Google Scholar PubMed
13. Lohan KS, Griffiths SS, Sciutti A, Partmann TC, Rohlfing KJ. Co-development of manner and path concepts in language, action, and eye-gaze behavior. Top Cognit Sci 2014;6:492–512.10.1111/tops.12098Search in Google Scholar PubMed
14. Gori M, Sandini G, Burr D. Development of visuo-auditory integration in space and time. Front Integr Neurosci 2012;6:77.10.3389/fnint.2012.00077Search in Google Scholar PubMed PubMed Central
15. Samadani AA, Moussavi Z. The effect of aging on human brain spatial processing performance. Conf Proc IEEE Eng Med Biol Soc 2012;2012:6768–71.10.1109/EMBC.2012.6347548Search in Google Scholar PubMed
16. Chauhan A, Seabra Lopes L. Using spoken words to guide open-ended category formation. Cognit Process 2011;12:341–54.10.1007/s10339-011-0407-ySearch in Google Scholar PubMed
17. Tinelli F, Anobile G, Gori M, Aagten-Murphy D, Bartoli M, Burr DC, et al. Time, number and attention in very low birth weight children. Neuropsychologia 2015;73:60–9.10.1016/j.neuropsychologia.2015.04.016Search in Google Scholar PubMed PubMed Central
18. Bisio A, Avanzino L, Gueugneau N, Pozzo T, Ruggeri P, Bove M. Observing and perceiving: A combined approach to induce plasticity in human motor cortex. Clin Neurophysiol 2015;126:1212–20.10.1016/j.clinph.2014.08.024Search in Google Scholar PubMed
19. Sparaci L, Formica D, Lasorsa FR, Mazzone L, Valeri G, Vicari S. Untrivial pursuit: measuring motor procedures learning in children with autism. Autism Res 2015;8:398–411.10.1002/aur.1455Search in Google Scholar PubMed
20. den Brok WL, Sterkenburg PS. Self-controlled technologies to support skill attainment in persons with an autism spectrum disorder and/or an intellectual disability: a systematic literature review. Disabil Rehabil Assist Technol 2015;10:1–10.10.3109/17483107.2014.921248Search in Google Scholar PubMed
21. Schoepflin ZR, Chen X, Ragonesi CB, Galloway JC, Agrawal SK. Design of a novel mobility device controlled by the feet motion of a standing child: a feasibility study. Med Biol Eng Comput 2011;49:1225–31.10.1007/s11517-011-0820-5Search in Google Scholar PubMed
22. Dautenhahn K. Socially intelligent robots: dimensions of human-robot interaction. Philos Trans R Soc Lond B Biol Sci 2007;362:679–704.10.1098/rstb.2006.2004Search in Google Scholar PubMed PubMed Central
23. Weigmann K. Robots emulating children. Scientists are developing robots using biology as their inspiration. Will they succeed in building cognitive agents? EMBO Rep 2006;7:474–6.10.1038/sj.embor.7400694Search in Google Scholar PubMed PubMed Central
24. Pearson Y, Borenstein J. The intervention of robot caregivers and the cultivation of children’s capability to play. Sci Eng Ethics 2013;19:123–37.10.1007/s11948-011-9309-8Search in Google Scholar PubMed
25. Encarnação P, Alvarez L, Rios A, Maya C, Adams K, Cook A. Using virtual robot-mediated play activities to assess cognitive skills. Disabil Rehabil Assist Technol 2014;9:231–41.10.3109/17483107.2013.782577Search in Google Scholar PubMed
26. Taffoni F, Formica D, Campolo D, Keller F, Guglielmelli E. Block-box instrumented toy: a new platform for assessing spatial cognition in infants. Conf Proc IEEE Eng Med Biol Soc 2009;2009:210–3.10.1109/IEMBS.2009.5333127Search in Google Scholar PubMed
27. Shimoda M. Brain, mind, body and society: autonomous system in robotics. J Int Bioethique 2013;24:41–8, 178–9.10.3917/jib.243.0039Search in Google Scholar PubMed
28. Costescu CA, Vanderborght B, David DO. Reversal learning task in children with autism spectrum disorder: a robot-based approach. J Autism Dev Disord 2015;45:3715–25.10.1007/s10803-014-2319-zSearch in Google Scholar PubMed
29. Giannopulu I. Multimodal interactions in typically and atypically developing children: natural versus artificial environments. Cognit Process 2013;14:323–31.10.1007/s10339-013-0566-0Search in Google Scholar PubMed
30. Jordan K, King M, Hellersteth S, Wirén A, Mulligan H. Feasibility of using a humanoid robot for enhancing attention and social skills in adolescents with autism spectrum disorder. Int J Rehabil Res 2013;36:221–7.10.1097/MRR.0b013e32835d0b43Search in Google Scholar PubMed
31. Moriguchi Y, Kanda T, Ishiguro H, Itakura S. Children perseverate to a human’s actions but not to a robot’s actions. Dev Sci 2010;13:62–8.10.1111/j.1467-7687.2009.00860.xSearch in Google Scholar PubMed
32. Douglas J, Reeson B, Ryan M. Computer microtechnology for a severely disabled preschool child. Child Care Health Dev 1988;14:93–104.10.1111/j.1365-2214.1988.tb00566.xSearch in Google Scholar
33. Sage KD, Baldwin D. Social gating and pedagogy: mechanisms for learning and implications for robotics. Neural Netw 2010;23:1091–8.10.1016/j.neunet.2010.09.004Search in Google Scholar
34. Masia L, Frascarelli F, Morasso P, Di Rosa G, Petrarca M, Castelli E, et al. Reduced short term adaptation to robot generated dynamic environment in children affected by cerebral palsy. J Neuroeng Rehabil 2011;8:28.10.1186/1743-0003-8-28Search in Google Scholar
35. Masia L, Frascarelli F, Morasso P, Di Rosa G, Petrarca M, Castelli E, et al. Abnormal adaptation in children affected by cerebral palsy to robot generated dynamic environment. Conf Proc IEEE Eng Med Biol Soc 2010;2010:3410–3.10.1109/IEMBS.2010.5627927Search in Google Scholar
36. Amirabdollahian F, Robins B, Dautenhahn K, Ji Z. Investigating tactile event recognition in child-robot interaction for use in autism therapy. Conf Proc IEEE Eng Med Biol Soc 2011;2011:5347–51.10.1109/IEMBS.2011.6091323Search in Google Scholar
37. Trafton JG, Harrison AM. Embodied spatial cognition. Top Cognit Sci 2011;3:686–706.10.1111/j.1756-8765.2011.01158.xSearch in Google Scholar
38. Metta G, Natale L, Nori F, Sandini G, Vernon D, Fadiga L, et al. The iCub humanoid robot: an open-systems platform for research in cognitive development. Neural Netw 2010;23:1125–34.10.1016/j.neunet.2010.08.010Search in Google Scholar
39. Kozima H, Nakagawa C, Yasuda Y. Children-robot interaction: a pilot study in autism therapy. Prog Brain Res 2007;164:385–400.10.1016/S0079-6123(07)64021-7Search in Google Scholar
40. Cook AM, Bentz B, Harbottle N, Lynch C, Miller B. School-based use of a robotic arm system by children with disabilities. IEEE Trans Neural Syst Rehabil Eng 2005;13:452–60.10.1109/TNSRE.2005.856075Search in Google Scholar PubMed
41. Cook AM, Meng MQ, Gu JJ, Howery K. Development of a robotic device for facilitating learning by children who have severe disabilities. IEEE Trans Neural Syst Rehabil Eng 2002;10:178–87.10.1109/TNSRE.2002.802877Search in Google Scholar PubMed
42. Smania N, Gandolfi M, Marconi V, Calanca A, Geroin C, Piazza S, et al. Applicability of a new robotic walking aid in a patient with cerebral palsy. Case report. Eur J Phys Rehabil Med 2012;48:147–53.Search in Google Scholar
43. Mikołajewska E, Mikołajewski D. Exoskeletons in neurological diseases – current and potential future applications. Adv Clin Exp Med 2011;20:227–33.Search in Google Scholar
44. Montesano L, Díaz M, Bhaskar S, Minguez J. Towards an intelligent wheelchair system for users with cerebral palsy. IEEE Trans Neural Syst Rehabil Eng 2010;18:193–202.10.1109/TNSRE.2009.2039592Search in Google Scholar PubMed
45. Williams L, Jackson CP, Choe N, Pelland L, Scott SH, Reynolds JN. Sensory-motor deficits in children with fetal alcohol spectrum disorder assessed using a robotic virtual reality platform. Alcohol Clin Exp Res 2014;38:116–25.10.1111/acer.12225Search in Google Scholar PubMed
46. Cardoso-Leite P, Bavelier D. Video game play, attention, and learning: how to shape the development of attention and influence learning? Curr Opin Neurol 2014;27:185–91.10.1097/WCO.0000000000000077Search in Google Scholar PubMed
47. Labruyère R, Gerber CN, Birrer-Brütsch K, Meyer-Heim A, van Hedel HJ. Requirements for and impact of a serious game for neuro-pediatric robot-assisted gait training. Res Dev Disabil 2013;34:3906–15.10.1016/j.ridd.2013.07.031Search in Google Scholar PubMed
48. Wass SV, Porayska-Pomsta K. The uses of cognitive training technologies in the treatment of autism spectrum disorders. Autism 2014;18:851–71.10.1177/1362361313499827Search in Google Scholar PubMed
49. Wójcik GM, Kaminski WA. Liquid state machine and its separation ability as function of electrical parameters of cell. Neurocomputing 2007;70:2593–7.10.1016/j.neucom.2006.12.015Search in Google Scholar
50. Wojcik GM. Electrical parameters influence on the dynamics of the Hodgkin-Huxley liquid state machine. Neurocomputing 2012;79:68–74.10.1016/j.neucom.2011.10.007Search in Google Scholar
51. Angryk R, Czerniak J. Heuristic algorithm for interpretation of multi-valued attributes in similarity-based fuzzy relational databases. Int J Approx Reason 2010;51:895–911.10.1016/j.ijar.2010.05.001Search in Google Scholar
52. Czerniak JM, Apiecionek Ł, Zarzycki H. Application of ordered fuzzy numbers in a new OFNAnt algorithm based on ant colony optimization. Commun Comput Inf Sci 2014;424:259–70.10.1007/978-3-319-06932-6_25Search in Google Scholar
53. Mikolajewska E, Mikolajewski D. E-learning in the education of people with disabilities. Adv Clin Exp Med 2011;20:103–9.Search in Google Scholar
54. Mikolajewska E, Mikolajewski D. The prospects of brain-computer interface applications in children. Cent Eur J Med 2014;9:74–9.10.2478/s11536-013-0249-3Search in Google Scholar
55. Mikołajewska E, Mikołajewski D. Bobath method in rehabilitation of adults and children [article in Polish]. Niepełnosprawność – zagadnienia, problemy, rozwiązania 2016;I:7–23.Search in Google Scholar
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