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Edge Computing based Human-Robot Cognitive Fusion: A Medical Case Study in the Autism Spectrum Disorder Therapy (2401.00776v2)

Published 1 Jan 2024 in cs.RO, cs.AI, cs.DC, cs.LG, and cs.MA

Abstract: In recent years, edge computing has served as a paradigm that enables many future technologies like AI, Robotics, IoT, and high-speed wireless sensor networks (like 5G) by connecting cloud computing facilities and services to the end users. Especially in medical and healthcare applications, it provides remote patient monitoring and increases voluminous multimedia. From the robotics angle, robot-assisted therapy (RAT) is an active-assistive robotic technology in rehabilitation robotics, attracting researchers to study and benefit people with disability like autism spectrum disorder (ASD) children. However, the main challenge of RAT is that the model capable of detecting the affective states of ASD people exists and can recall individual preferences. Moreover, involving expert diagnosis and recommendations to guide robots in updating the therapy approach to adapt to different statuses and scenarios is a crucial part of the ASD therapy process. This paper proposes the architecture of edge cognitive computing by combining human experts and assisted robots collaborating in the same framework to achieve a seamless remote diagnosis, round-the-clock symptom monitoring, emergency warning, therapy alteration, and advanced assistance.

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References (41)
  1. Mobile edge computing: opportunities, solutions, and challenges.
  2. The integrative power of cognitive therapy.
  3. Cognitive developmental robotics: A survey. IEEE transactions on autonomous mental development, 1(1): 12–34.
  4. Follow me fog: Toward seamless handover timing schemes in a fog computing environment. IEEE Communications Magazine, 55(11): 72–78.
  5. Beck, A. T. 1964. Thinking and depression: II. Theory and therapy. Archives of general psychiatry, 10(6): 561–571.
  6. Beck, J. S. 2020. Cognitive behavior therapy: Basics and beyond. Guilford Publications.
  7. Bernet, W. 1993. Humor in evaluating and treating children and adolescents. The Journal of Psychotherapy Practice and Research, 2(4): 307.
  8. A fog computing approach for localization in WSN. In 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 1–7. IEEE.
  9. Deep learning with edge computing: A review. Proceedings of the IEEE, 107(8): 1655–1674.
  10. Edge cognitive computing based smart healthcare system. Future Generation Computer Systems, 86: 403–411.
  11. Long-term personalization of an in-home socially assistive robot for children with autism spectrum disorders. Frontiers in Robotics and AI, 6: 110.
  12. Behavior trees in robotics and AI: An introduction. CRC Press.
  13. The clinical use of robots for individuals with autism spectrum disorders: A critical review. Research in autism spectrum disorders, 6(1): 249–262.
  14. Ellis, A. 1962. Reason and emotion in psychotherapy.
  15. Fein, G. G. 1981. Pretend play in childhood: An integrative review. Child development, 1095–1118.
  16. Freud, S. 1960. Jokes and their relation to the unconscious. WW Norton & Company.
  17. Edge robotics: Are we ready? An experimental evaluation of current vision and future directions. Digital Communications and Networks, 9(1): 166–174.
  18. Edge computing in smart health care systems: Review, challenges, and research directions. Transactions on Emerging Telecommunications Technologies, 33(3): e3710.
  19. The efficacy of cognitive behavioral therapy: A review of meta-analyses. Cognitive therapy and research, 36: 427–440.
  20. Edge computing: A survey. Future Generation Computer Systems, 97: 219–235.
  21. Edge Computing: An overview of framework and applications.
  22. Humor: Its origin and development. WH Freeman San Francisco.
  23. Merrick, K. 2017. Value systems for developmental cognitive robotics: A survey. Cognitive Systems Research, 41: 38–55.
  24. Merrick, K. E. 2013. Novelty and beyond: Towards combined motivation models and integrated learning architectures. Intrinsically motivated learning in natural and artificial systems, 209–233.
  25. An edge computing based smart healthcare framework for resource management. Sensors, 18(12): 4307.
  26. Human development. McGraw-Hill.
  27. Cognitive distortions, humor styles, and depression. Europe’s journal of psychology, 12(3): 348.
  28. Cloudlet deployment in local wireless networks: Motivation, architectures, applications, and open challenges. Journal of Network and Computer Applications, 62: 18–40.
  29. Southam, M. 2005. Humor development: An important cognitive and social skill in the growing child. Physical & Occupational Therapy in Pediatrics, 25(1-2): 105–117.
  30. Plasticity in value systems and its role in adaptive behavior. Adaptive Behavior, 8(2): 129–148.
  31. Cognitive computing and wireless communications on the edge for healthcare service robots. Computer Communications, 149: 99–106.
  32. Convergence of edge computing and deep learning: A comprehensive survey. IEEE Communications Surveys & Tutorials, 22(2): 869–904.
  33. Yang, Q. 2023. Hierarchical Needs-driven Agent Learning Systems: From Deep Reinforcement Learning To Diverse Strategies. The 37th AAAI 2023 Conference on Artificial Intelligence and Robotics Bridge Program.
  34. Understanding the Application of Utility Theory in Robotics and Artificial Intelligence: A Survey. arXiv preprint arXiv:2306.09445.
  35. Self-reactive planning of multi-robots with dynamic task assignments. In 2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), 89–91. IEEE.
  36. Hierarchical needs based self-adaptive framework for cooperative multi-robot system. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2991–2998. IEEE.
  37. Needs-driven heterogeneous multi-robot cooperation in rescue missions. In 2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), 252–259. IEEE.
  38. How can robots trust each other for better cooperation? a relative needs entropy based robot-robot trust assessment model. In 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2656–2663. IEEE.
  39. A Hierarchical Game-Theoretic Decision-Making for Cooperative Multiagent Systems Under the Presence of Adversarial Agents. In Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, SAC ’23, 773–782. New York, NY, USA: Association for Computing Machinery. ISBN 9781450395175.
  40. A Strategy-Oriented Bayesian Soft Actor-Critic Model. Procedia Computer Science, 220: 561–566.
  41. Edge computing robot interface for automatic elderly mental health care based on voice. Electronics, 9(3): 419.

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