International Science Index


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.
[1] D. Foti and J. S. Koketsu, “Activities of daily living,” Pedrettis Occupational Therapy: Practical Skills for Physical Dysfunction, vol. 7, pp. 157–232, 2013.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] W.-K. Song and J. Kim, “Novel assistive robot for self-feeding,” in Robotic Systems-Applications, Control and Programming. InTech, 2012.
[7] 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.
[8] OBI. (2016) Tech specs. (Online). Available:
[9] 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.
[10] M. Topping, “Flexibot–a multi-functional general purpose service robot,” Industrial Robot: An International Journal, vol. 28, no. 5, pp. 395–401, 2001.
[11] H. Kwee and C. Stanger, “The manus robot arm,” Rehabilitation Robotics Newsletter, vol. 5, no. 2, pp. 1–2, 1993.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] X. Xie, M. Jones, and G. Tam, “Recognition, tracking, and optimisation,” International Journal of Computer Vision, vol. 122, no. 3, pp. 409–410, 2017.
[20] 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.