Commenced in January 2007
Paper Count: 31108
Mathematical Description of Functional Motion and Application as a Feeding Mode for General Purpose Assistive Robots
Abstract:Eating a meal is among the Activities of Daily Living, but it takes a lot of time and effort for people with physical or functional limitations. Dedicated technologies are cumbersome and not portable, while general-purpose assistive robots such as wheelchair-based manipulators are too hard to control for elaborate continuous motion like eating. Eating with such devices has not previously been automated, since there existed no description of a feeding motion for uncontrolled environments. In this paper, we introduce a feeding mode for assistive manipulators, including a mathematical description of trajectories for motions that are difficult to perform manually such as gathering and scooping food at a defined/desired pace. We implement these trajectories in a sequence of movements for a semi-automated feeding mode which can be controlled with a very simple 3-button interface, allowing the user to have control over the feeding pace. Finally, we demonstrate the feeding mode with a JACO robotic arm and compare the eating speed, measured in bites per minute of three eating methods: a healthy person eating unaided, a person with upper limb limitations or disability using JACO with manual control, and a person with limitations using JACO with the feeding mode. We found that the feeding mode allows eating about 5 bites per minute, which should be sufficient to eat a meal under 30min.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1316464Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 663
 D. Foti and J. S. Koketsu, “Activities of daily living,” Pedrettis Occupational Therapy: Practical Skills for Physical Dysfunction, vol. 7, pp. 157–232, 2013.
 I. Naotunna, C. J. Perera, C. Sandaruwan, R. Gopura, and T. D. Lalitharatne, “Meal assistance robots: A review on current status, challenges and future directions,” in System Integration (SII), 2015 IEEE/SICE International Symposium on. IEEE, 2015, pp. 211–216.
 W. T. Latt, T. P. Luu, C. Kuah, and A. W. Tech, “Towards an upper-limb exoskeleton system for assistance in activities of daily living (adls),” in Proceedings of the international Convention on Rehabilitation Engineering & Assistive Technology. Singapore Therapeutic, Assistive & Rehabilitative Technologies (START) Centre, 2014, p. 12.
 S. Ishii, S. Tanaka, and F. Hiramatsu, “Meal assistance robot for severely handicapped people,” in Robotics and Automation, 1995. Proceedings., 1995 IEEE International Conference on, vol. 2. IEEE, 1995, pp. 1308–1313.
 S. W. Brose, D. J. Weber, B. A. Salatin, G. G. Grindle, H. Wang, J. J. Vazquez, and R. A. Cooper, “The role of assistive robotics in the lives of persons with disability,” American Journal of Physical Medicine & Rehabilitation, vol. 89, no. 6, pp. 509–521, 2010.
 W.-K. Song and J. Kim, “Novel assistive robot for self-feeding,” in Robotic Systems-Applications, Control and Programming. InTech, 2012.
 J. J. Villarreal and S. Ljungblad, “Experience centred design for a robotic eating aid,” in Human-Robot Interaction (HRI), 2011 6th ACM/IEEE International Conference on. IEEE, 2011, pp. 155–156.
 OBI. (2016) Tech specs. (Online). Available: https://meetobi.com/tech-specs/.
 M. J. Topping and J. K. Smith, “The development of handy 1. a robotic system to assist the severely disabled,” Technology and Disability, vol. 10, no. 2, pp. 95–105, 1999.
 M. Topping, “Flexibot–a multi-functional general purpose service robot,” Industrial Robot: An International Journal, vol. 28, no. 5, pp. 395–401, 2001.
 H. Kwee and C. Stanger, “The manus robot arm,” Rehabilitation Robotics Newsletter, vol. 5, no. 2, pp. 1–2, 1993.
 F. Routhier, P. Archambault, M. Cyr, V. Maheu, M. Lemay, and I. G´elinas, “Benefits of jaco robotic arm on independent living and social participation: an exploratory study,” in RESNA Annual Conference, 2014.
 V. Maheu, P. S. Archambault, J. Frappier, and F. Routhier, “Evaluation of the jaco robotic arm: Clinico-economic study for powered wheelchair users with upper-extremity disabilities,” in Rehabilitation Robotics (ICORR), 2011 IEEE International Conference on. IEEE, 2011, pp. 1–5.
 Y. Cheng, X. Zhao, R. Cai, Z. Li, K. Huang, and Y. Rui, “Semi-supervised multimodal deep learning for rgb-d object recognition.” in IJCAI, 2016, pp. 3345–3351.
 J. Lahoud and B. Ghanem, “2d-driven 3d object detection in rgb-d images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4622–4630.
 A. Campeau-Lecours, V. Maheu, S. Lepage, H. Lamontagne, S. Latour, L. Paquet, and N. Hardie, “Jaco assistive robotic device: Empowering people with disabilities through innovative algorithms,” in Rehabilitation Engineering and Assistive Technology Society of North America (RESNA) an-255 nual conference, vol. 14, 2016.
 C.-S. Chung, H. Wang, and R. A. Cooper, “Functional assessment and performance evaluation for assistive robotic manipulators: Literature review,” The journal of spinal cord medicine, vol. 36, no. 4, pp. 273–289, 2013.
 L. V. Herlant, R. M. Holladay, and S. S. Srinivasa, “Assistive teleoperation of robot arms via automatic time-optimal mode switching,” in Human-Robot Interaction (HRI), 2016 11th ACM/IEEE International Conference on. IEEE, 2016, pp. 35–42.
 X. Xie, M. Jones, and G. Tam, “Recognition, tracking, and optimisation,” International Journal of Computer Vision, vol. 122, no. 3, pp. 409–410, 2017.
 H. Jiang, J. P. Wachs, and B. S. Duerstock, “Integrated vision-based robotic arm interface for operators with upper limb mobility impairments,” in Rehabilitation Robotics (ICORR), 2013 IEEE International Conference on. IEEE, 2013, pp. 1–6.