Hostname: page-component-8448b6f56d-dnltx Total loading time: 0 Render date: 2024-04-24T15:49:23.735Z Has data issue: false hasContentIssue false
  • English
  • Français

Robotics Agent Coacher for CP motor Function (RAC CP Fun)

Published online by Cambridge University Press:  07 August 2014

Marina Fridin  and
Mark Belokopytov
Marina Fridin*
Affiliation:
Faculty of Industrial Engineering and Management, Ariel University Center, POB 3, Kiryat Hamada, Ariel, 40700, Israel
Mark Belokopytov
Affiliation:
Human Motion Analysis Laboratory, Assaf Harofeh Medical Center, Zerefin, Israel60930
*
*Corresponding author: E-mail: marinafridin@gmail.com
Get access Rights & Permissions [Opens in a new window]

Summary

Robotics Agent Coacher for Cerebral Palsy motor Function (RAC CP Fun) is an attempt to implement socially assistive robotics, and a motor learning approach in rehabilitating movement disorders with a central origin. The concept and architecture of RAC CP Fun implements the motor learning theory and behavioral approach, i.e. principles of repetition, stages of learning, appropriate feedback, random practice, and enriched environments. Eleven children with cerebral palsy (CP) and fourteen typically developed (TD) children participated in two procedures while interacting with a robot and performing motor exercises. The interaction level and motor performance of children were measured and compared. Children with CP exhibited a higher interaction level; however, their motor performance was lower than that of TD children. RAC CP Fun was found to be feasible to interact with children of pre-school age, to augment the motivation of the children with CP, and to involve the children in motor exercises.

Keywords

Social assitive roboticsCerebral palsy
Type
Articles
Information
Robotica , Volume 32 , Issue 8: Rehabilitation Robotics and Human-Robot Interaction , December 2014 , pp. 1265 - 1279
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Bax, M. C., Flodmark, O. and Tydeman, C., “Definition and classification of cerebral palsy. From syndrome toward disease,” Dev. Med. Child Neural 109, 39–41 (2007).CrossRefGoogle ScholarPubMed
2.Bruck, I., Antoniuk, S. A., Spessatto, A., Bem, R. S., Hausberger, R. and Pacheco, C. G., “Epilepsy in children with cerebral palsy,” Arq. Neuropsiquiatr. 59, 35–39 (2001).CrossRefGoogle ScholarPubMed
3.Sigurdardottir, S. and Vik, T., “Speech, expressive language, and verbal cognition of preschool children with cerebral palsy in Iceland,” Dev. Med. Child Neural 53, 74–80 (2011).CrossRefGoogle ScholarPubMed
4.Peet, D. S., “Retrospective review of the epidemiology of epilepsy in special schools for children with cerebral palsy, learning difficulties, and language and communication difficulties,” Mcgill J. Med. 9, 19–23 (2006).Google ScholarPubMed
5.Aisen, M. L., Kerkovich, D., Mast, J., Mulroy, S., Wren, T. A., Kay, R. M. and Rethlefsen, S. A., “Cerebral palsy: clinical care and neurological rehabilitation,” Lancet Neurol 10, 844–852 (2011).CrossRefGoogle ScholarPubMed
6.Holt, R. L. and Mikati, M. A., “Care for child development: basic science rationale and effects of interventions,” Pediatr. Neurol. 44, 239–253 (2011).CrossRefGoogle ScholarPubMed
7.Shi, Y. X., Tian, J. H., Yang, K. H. and Zhao, Y., “Modified constraint-induced movement therapy versus traditional rehabilitation in patients with upper-extremity dysfunction after stroke: a systematic review and meta-analysis,” Arch. Phys. Med. Rehabil. 92, 972–982 (2011).CrossRefGoogle ScholarPubMed
8.Lo, A. C., Guarino, P. D., Richards, L. G., Haselkorn, J. K., Wittenberg, G. F., Federman, D. G., Ringer, R. J., Wagner, T. H., Krebs, H. I., Volpe, B. T., Bever, C. T., Bravata, D. M., Duncan, P. W., Corn, B. H., Maffucci, A. D., Nadeau, S. E., Conroy, S. S., Powell, J. M., Huang, G. D. and Peduzzi, P., “Robot-assisted therapy for long-term upper-limb impairment after stroke,” N. Engl. J. Med. 362, 1772–1783 (2010).CrossRefGoogle ScholarPubMed
9.Combs, S. A., Dugan, E. L., Passmore, M., Riesner, C., Whipker, D., Yingling, E. and Curtis, A. B., “Balance, balance confidence, and health-related quality of life in persons with chronic stroke after body weight-supported treadmill training,” Arch. Phys. Med. Rehabil. 91, 1914–1919 (2010).CrossRefGoogle ScholarPubMed
10.Bar-Haim, S., Harries, N., Nammourah, I., Oraibi, S., Malhees, W., Loeppky, J., Perkins, N. J., Belokopytov, M., Kaplanski, J. and Lahat, E., “MERC project: Effectiveness of motor learning coaching in children with cerebral palsy: a randomized controlled trial,” Clin. Rehabil. 24, 1009–1020 (2010).CrossRefGoogle ScholarPubMed
11.Schmidt, R. A. and Lee, T. D., Motor Control and Learning 3rd ed. (Champaign, IL; Human Kinetics: 1999).Google Scholar
12.McGaugh, J. L., “Make mild moments memorable: add a little arousal,” Trends Cogn. Sci. 10, 345–347 (2006).CrossRefGoogle ScholarPubMed
13.McGaugh, J. L., “Memory-a century of consolidation,” Science 287, 248–251 (2000).CrossRefGoogle ScholarPubMed
14.Schmidt, R. A. and Wrisberg, C. A., Motor Learning and Performance 3rd Edition (Champaign, IL: Human Kinetics, 2004).Google Scholar
15.Sullivan, K. J., Kantak, S. S. and Burtner, P. A., “Motor learning in children: feedback effects on skill acquisition,” Phys. Ther. 88, 720–732 (2008).CrossRefGoogle ScholarPubMed
16.Weinstein, C. J., “Knowledge of results and motor learning–implications for physical therapy,” Phys. Ther. 71, 140–149 (1991).CrossRefGoogle Scholar
17.Mihelj, M., Novak, D. and Munih, M., “Emotion-aware system for upper extremity rehabilitation,” Proceedings of the Virtual Rehabilitation International Conference 2009; Haifa, Israel. Edited by Haifa, Weiss P.: ACM IEEE (2009) pp. 160–165.CrossRefGoogle Scholar
18.Feil-Seifer, D. and Matarić, M., “Defining socially assistive robotics.” Proceedings of the IEEE International Conference on Rehabilitation Robotics (ICORR-05), Chicago, IL; (2005) pp. 465–468.Google Scholar
19.Feil-Seifer, D. and Matarić, M. J., “Automated detection and classification of positive vs. negative robot interactions with children with autism using distance-based features” Proceedings of the 6th international conference on Human-robot interaction (HRI '11); Lausanne, Switzerland. Edited by Lausanne, Billard A.: ACM IEEE, (2011) pp. 323–330.Google Scholar
20.Shim, J. and Thomaz, A. L., “Human-like action segmentation for option learning” Proceedings of the 20th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN),. Atlanta, Georgia. Edited by Scheutz M, Christensen HI. ACM IEEE; (2011) pp. 455–460.Google Scholar
21.Lee, J., Kieser, J. F., Bobick, A. F. and Thomaz, A. L., “Vision-based contingency detection,” Proceedings of the 6th international conference on Human-robot interaction (HRI '11); Lausanne, Switzerland. Edited by Lausanne, Billard A.: ACM IEEE; (2011) pp. 297–304.Google Scholar
22.Klamer, T., Allouch, S. and Heylen, D., “Adventures of Harvey”-Use, acceptance of and relationship building with a social robot in a domestic environment,” Soc. Inform. Telecommun. Eng. 59, 74–82 (2011).Google Scholar
23.Heerink, M., Krse, B., Evers, V. and Wielinga, B., “Responses to a social robot by elderly users,” Intell. Robots Syst., Nice, France. ACM IEEE; 2724, 22–26 (2008).Google Scholar
24.Nikolopoulos, C., Kuester, D., Sheehan, M., Ramteke, S., Karmarkar, A., Thota, S., Kearney, J., Boirum, C., Bojedla, S. and Lee, A., “Robotic agents used to help teach social skills to children with autism: the third generation,” Proceedings of the 20th IEEE International Symposium on Robot and Human Interactive Communication, Atlanta, Georgia. Edited by Scheutz M, Christensen HI. ACM IEEE; (2011) pp. 253–258.Google Scholar
25.Goodrich, M. A., Colton, M. A., Brinton, B. and Fujiki, M., “A case for low-dose robotics in autism therapy,” Proceedings of the 6th international conference on human-robot interaction (HRI '11), Lausanne, Switzerland. Edited by Lausanne, Billard A.: ACM IEEE, 59, (2011) pp. 143–144.Google Scholar
26.Villano, M., Crowell, C. R., Wier, K., Tang, K., Thomas, B., Shea, N., Schmitt, L. M. and Diehl, J. J., “DOMER: a wizard of oz interface for using interactive robots to scaffold social skills for children with autism spectrum disorders,” Proceedings of the 6th international conference on Human-robot interaction (HRI '11); Lausanne, Switzerland. Edited by Lausanne, Billard A.: ACM IEEE, (2011) pp. 279–280.Google Scholar
27.Pop, C., Simut, R., Pinte, S., Saldien, J., Rusu, A., Vanderfaeillie, J., David, D., Lefeber, D., and Vanderborght, B., “Social robots vs. computer display: does the way social stories are delivered make a difference for their effectiveness on ASD children?,” J. Educ. Comput. Res. (2012).CrossRefGoogle Scholar
28.Thill, S., Pop, C., Belpaeme, T., Ziemke, T., and Vanderborght, B., “Robot-assisted therapy with (partially) autonomous control: challenges and outlook,” PALADYN J. Behav. Robot. 3, 209–217 (2012).Google Scholar
29.Vanderborght, B., Simut, R., Saldien, J., Pop, C., Rusu, A., Pintea, S., Lefeber, D., and David, D., “Using the social robot probo as social story telling agent for children with ASD,”, Interact. stud. 13, 348–372 (2012).CrossRefGoogle Scholar
30.Shukla-Mehta, S., Miller, T. and Callahan, K. J., “Evaluating the effectiveness of video instruction on social and communication skills training for children with autism spectrum disorders: A review of the literature,” Focus Autism Dev. Disabl. 25, 23–36, (2009).CrossRefGoogle Scholar
31.Tartaro, A. and Cassell, J., “Playing with virtual peers: bootstrapping contingent discourse in children with autism,” Proceedings of International Conference of the Learning Sciences (ICLS):, Utrecht, Netherlands; ACM Press, 2, (2008).Google Scholar
32.Frascarelli, F., Masia, L., DiAAAARosa, G., Cappa, P., Petrarca, M., Castelli, E. and Krebs, H. I., “The impact of robotic rehabilitation in children with acquired or congenital movement disorders,” Eur. J. Phys. Rehabil. Med., 45, 135–141 (2009).Google ScholarPubMed
33.Kronreif, G., Prazak, B., Mina, S., Kornfeld, M., Meindl, M., and Furst, F., “Playrob–robot-assisted playing for children with severe physical disabilities,” Proceedings of the IEEE 9th International Conference on Rehabilitation Robotics 2005 (ICORR'05), (2005) pp. 193–196.Google Scholar
34.Cook, A. M., Bentz, B., Harbottle, N., Lynch, C. and Miller, B., “School-based use of a robotic arm system by children with disabilities,” Neural Syst. Rehabil. Eng. 13, 452–60 (2005).Google Scholar
35.Topping, M., “An overview of the development of handy 1, a rehabilitation robot to assist the severely disabled,” J. Intell. Robot. Syst. 34, 253–263, (2002).CrossRefGoogle Scholar
36.Krebs, H. I., Ladenheim, B., Hippolyte, C., Monterroso, L. and Mast, J., “Robot-assisted task-specific training in cerebral palsy,” Dev. Med. Child Neurol., 51, 140–145 (2009).CrossRefGoogle ScholarPubMed
37.Fasoli, S. E., Fragala-Pinkham, M., Hughes, R., Krebs, H. I., Hogan, N. and Stein, J., “Robotic therapy and botulinum toxin type A: a novel intervention approach for cerebral palsy,” Am. J. Phys. Med. Rehabil. 87, 1022–1025 (2008).CrossRefGoogle ScholarPubMed
38.Gregory, J., Howard, A., and Boonthum-Denecke, C., “Wii Nunchuk controlled dance pleo! dance! to assist children with cerebral palsy by play therapy,” In McCarthy, Philip M., Youngblood, Michael (Eds.): Proceedings of the 25th International Florida Artificial Intelligence Research Society (FLAIRS) Conference, 517–520 (Menlo Park, CA: The AAAI, 2012).Google Scholar
39.Jones, M., Trapp, T., Jones, N., Brooks, D., and Howard, A. M., “Engaging children with severe physical disabilities via teleoperated control of a robot piano player,” Proceedings of the 12th International ACM SIGACCESS Conference on Computers and Accessibility (2010) pp. 25–27.Google Scholar
40.Roberts, L., Park, H. W. and Howard, A. M., “Robots and therapeutic play: evaluation of a wireless interface device for interaction with a robot playmate,” Conf Proc IEEE Eng Med Biol Soc. (2012) pp. 6475–6478.Google Scholar
41.Brooks, D., Yu-ping, C., and Howard, A. M., “Simulation versus embodied agents: Does either induce better human adherence to physical therapy exercise?,” Biomedical Robotics and Biomechatronics (BioRob), 4th IEEE RAS and EMBS International (2012) pp. 1715–1720.Google Scholar
42.Lathan, C., Brisben, A., and Safos, C., “CosmoBot levels the playing field for disabled children,” Interactions 12, 14–16 (2005).CrossRefGoogle Scholar
43.World Health Organization: International Classification of Functioning, Disability and Health (ICF) (WHO, Geneva; 2001).Google Scholar
44.Vezhnevets, V., Sazonov, V. and Andreeva, A., “A survey on pixel-based skin color detection techniques,” Proc. Graphicon 85–92 (2003).Google Scholar
45.Canny, J.. “A computational approach to edge detection,” Proceedings of the IEEE Transactions on Pattern Analysis and Machine Intelligence archive 8 (IEEE Computer Society Washington, DC, USA, 1986).Google Scholar
46.Anderson, B. L., “Stereovision: Beyond disparity computations,” Trends Cogn. Sci. 2, 214–222 (1998).CrossRefGoogle ScholarPubMed
47.Berg, A. R. and Jordán, K., “Algorithms for graph rigidity and scene analysis,” Lect. Notes Comput. Sci. 2832, 78–89 (2003).CrossRefGoogle Scholar
48.Pol, D., Cuijpers, R. H. and Juola, J. F., “Head pose estimation for real-time low-resolution video,” ECCE '10 Proceedings of the 28th Annual European Conference on Cognitive Ergonomics (ACM, New York NY, USA, 2010).Google Scholar
49.Ousov-Fridin, M., deGelder, B. and Flash, T., “A computational model for body expression of emotion in HCI,” (University of Genova, Italy, 2006).Google Scholar
50.Liu, C., Conn, K., Sarkar, N. and Stone, W.. “Online affect detection and robot behavior adaptation for intervention of children with autism,” IEEE Trans. Robot. 24, (2008).Google Scholar
51.Leyvand, T., Meekhof, C., Sun, J. and Guo, B., “Kinect identity: technology and experience,” Computer 44, 94–96 (2011).CrossRefGoogle Scholar
52.MacTurk, R. H., Morgan, G. A., and Jennings, K. D., “The assessment of mastery motivation in infants and young children,” In: MacTurk, RH, Morgan, GA, editors. Mastery motivation: origin, conceptualizations and applications. pp. 19–56, (Norwood, NJ: Ablex Publishing Corporation, 1995).Google Scholar
53.Fridin, M. and Yaakobi, Y., “Educational robot for children with ADHD/ADD, Architectural design,” Proceedings of the International Conference on Computational Vision and Robotics (2011) pp. 21–22.Google Scholar
54.Gouaillier, D., Hugel, V., Blazevic, P., Kilner, C., Monceaux, J., Lafourcade, P., Marnier, B., Serre, J. and Maisonnier, B., “Mechatronic design of NAO humanoid,” Proceedings IEEE Int. Conf. on Robotics and Automation Kobe, Japan, (2009) pp. 769–774.Google Scholar
55.Nalin, M., Bergamini, L., Giusti, A., Baroni, I. and Sanna, A., “Children's perception of a robotic companion in a mildly constrained setting: How children within age 8–11 perceive a robotic companion,” Proceedings of the 6th international conference on Human-robot interaction (HRI '11) (Robots with Children Workshop) Lausanne, Switzerland. Edited by Lausanne, Billard A.: ACM IEEE; (2011) pp. 260–263.Google Scholar
56.Palisano, R., Rosenbaum, P., Walter, S., Russell, D., Wood, E. and Galuppi, B., “Development and reliability of a system to classify gross motor function in children with cerebral palsy,” Dev. Med. Child Neurol. 39, 214–223 (1997).CrossRefGoogle ScholarPubMed
57.Wolpe, J., Psychotherapy by Reciprocal Inhibition (California: Stanford University Press; 1958).Google Scholar
58.Robins, B., Otero, N., Ferrari, E. and Dautenhahn, K., “Eliciting requirements for a robotic toy for children with autism-results from user panels,” Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication Jeju, South Korea, (2007) pp. 101–106.Google Scholar
59.Swinnen, S. P., and Carson, R. G., “The control in learning of patterns of interlimb coordination: Past and present issues in normal and disordered control,” Acta Psychol. 110, 129–137 (2002).CrossRefGoogle ScholarPubMed
60.Belokopytov, M. and Fridin, M., “Motivation of children with cerebral palsy during motor involvement by RAC-CP Fun,” Proceedings of the Workshops IEEE/RSJ International Conference on Intelligent Robots and Systems (2012) pp. 40–45.Google Scholar
61.Fridin, M., Angel, H., and Azery, S, “Acceptance, interaction, and authority of educational robots: An ethnography study of child -robot interaction robot interaction,” IEEE Workshop on Advanced Robotics and Its Social Impacts (California, USA, 2011).Google Scholar
62.Fridin, M., Bar-Haim, S., and Belokopytov, M., “Robotics agent coacher for cp motor function (RAC CP Fun),” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, (San Francisco, California, USA, 2011).Google Scholar
63.Keren, G., Ben-David, A. and Fridin, M., “Kindergarten assistive robotics (KAR) as a tool for spatial cognition development in pre-school education,” 2012 Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2012) pp. 1084–1089.Google Scholar
64.Fridin, M., “Kindergarten social assistive robot: first meeting and ethical issues,” Comput. Hum. Behav. 30, 262–272 (2014).CrossRefGoogle Scholar
65.Fridin, M., “Storytelling by a kindergarten assistive robot: a tool for constructive learning in preschool education,” Comput. Educ. 70, 53–64 (2014).CrossRefGoogle Scholar
66.Darwin, C. R., The Expression of the Emotions In Man And Animals (John Murray, London, 1872).CrossRefGoogle Scholar
67.Brave, S., “Emotion in human-computer interaction,” The human-computer interaction handbook, 2003.Google Scholar
68.Breazeal, C., “A motivational system for regulating human-robot interaction,” Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence (1998) pp. 54–62.Google Scholar
69.Balomenos, T., Raouzaiou, A., Loannou, S., Drosopoulos, A., Karpouzis, K. and Kollias, S., “Emotion analysis in man-machine interaction systems,” Lect. Notes 3361, 318–328 (2004).Google Scholar
70.Chen, L. S., and Huang, T. S., “Emotional expressions in audiovisual human computer interaction,” Proc. IEEE ICME 1, 423–426 (2000).Google Scholar
71.De Silva, L. C. and Ng, P. C., “Bi-modal emotion recognition,” Proc. FG 332–335 (2000).Google Scholar
72.Jessen, S. and Kotz, S. A., “The temporal dynamics of processing emotions from vocal, facial, and bodily expressions,” Neuroimage 58, 665–674 (2011).CrossRefGoogle ScholarPubMed
73.de Gelder, B., and Bertelson, P., “Multisensory integration, perception and ecological validity,” Trends Cogn. Sci. 7, 460–467 (2003).CrossRefGoogle ScholarPubMed
74.Lang, A. and Friestad, M., “Emotion, hemispheric specialization and visual and verbal memory for television messages,” Commun. Res. 20, 647–670 (1993).CrossRefGoogle Scholar
75.Meeren, H., Van Heijnsbergen, C., and deAAAAGelder, B., “Rapid perceptual integration of facial expression and emotional body language,” Proc. Natl. Acad. Sci. USA 102, 16518–16523 (2005).CrossRefGoogle ScholarPubMed
76.Van den Stock, J., Righart, R., and deAAAAGelder, B., “Body expressions influence recognition of emotions in the face and voice,” Emotion (2007).CrossRefGoogle Scholar
77.Van den Stock, J., Grèzes, J., deAAAAGelder, B., “Human and animal sounds influence recognition of body language,” Brain Res. 1242, 185–190 (2008).CrossRefGoogle ScholarPubMed
78.Ueki, N., Morishima, S., Yamada, H., “Expression analysis/synthesis system based on emotion space constructed by multilayered neural network,” Syst. Comput. Jpn. 25 (1994).Google Scholar
79.Salter, T., Michaud, F. and Letourneau, D., “Using proprioceptive sensors for behavioral adaptation,” Proceedings Human-Robot Interaction Conference (2007) pp. 105–112.Google Scholar
80.Matarić, M., Eriksson, J., Feil-Seifer, D. and Winstein, C., “Socially assistive robotics for post-stroke rehabilitation,” J. Neuroeng. Rehabil. 19, 4–5 (2007).Google Scholar
81.Tapus, A., “Improving the quality of life of people with dementia through the use of socially assistive robots,” Advanced Technologies for Enhanced Quality of Life Edited by Stoica, A., Arslan, T., Huntsberger, T., Botez, P., Erdogan, A., El-Rayis, A. Los Alamitos (IEEE Computer Society Press, 2009).Google Scholar
82.Rogers, C.R., “Empathy: An unappreciated way of being,” TCP 5, 2–10 (1975).Google Scholar
83.Maclean, N., Pound, P., Wolfe, C. and Rudd, A., “Qualitative analysis of stroke patients' motivation for rehabilitation,” BMJ 321, 1051–1054 (2000).CrossRefGoogle ScholarPubMed
84.Bar-Haim, S., Harries, N., Belokopytov, M., Lahat, E. and Kaplanski, J., “Random perturbation: a potential aid in treatment of children with cerebral palsy,” Disabil. Rehabil. 30, 1420–1428 (2008).CrossRefGoogle ScholarPubMed
85.Shadmehr, R. and Mussa-Ivaldi, F. A., “Adaptive representation of dynamics during learning of a motor task,” J. Neurosci. 14, 3208–3224 (1994).CrossRefGoogle ScholarPubMed
86.Ketelaar, M., “Effects of a functional therapy program on motor abilities of children with cerebral palsy,” Phys. Ther. 81, 1534–1545 (2001).CrossRefGoogle ScholarPubMed
87.Fitts, P. M. and Posner, M. I., Learning And Skilled Performance In Human Performance (Belmont CA: Brock-Cole; 1967).Google Scholar