Mathematical Description of Functional Motion and Application as a Feeding Mode for General Purpose Assistive Robots

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.

Authors:

• Martin Leroux
• Sylvain Brisebois

Keywords:

• Assistive robotics
• Automated feeding
• Elderly care
• Trajectory design
• Human-Robot Interaction.

References:

[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:
https://meetobi.com/tech-specs/.
[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.