Data-Driven Haptic Perception for Robot-Assisted Dressing

Kapusta, Ariel; Yu, Wenhao; Bhattacharjee, Tapomayukh; Liu, C. Karen; Turk, Greg; Kemp, Charles C.

Title:

Data-Driven Haptic Perception for Robot-Assisted Dressing

dc.contributor.author	Kapusta, Ariel
dc.contributor.author	Yu, Wenhao
dc.contributor.author	Bhattacharjee, Tapomayukh
dc.contributor.author	Liu, C. Karen
dc.contributor.author	Turk, Greg
dc.contributor.author	Kemp, Charles C.
dc.contributor.corporatename	Georgia Institute of Technology. Rehabilitation Engineering Research Center on Technologies to Support Successful Aging With Disability	en_US
dc.contributor.corporatename	Georgia Institute of Technology. Healthcare Robotics Lab	en_US
dc.contributor.corporatename	Georgia Institute of Technology. School of Interactive Computing	en_US
dc.date.accessioned	2017-02-21T14:11:48Z
dc.date.available	2017-02-21T14:11:48Z
dc.date.issued	2016-08
dc.description	© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.	en_US
dc.description	DOI: 10.1109/ROMAN.2016.7745158	en_US
dc.description.abstract	Dressing is an important activity of daily living (ADL) with which many people require assistance due to impairments. Robots have the potential to provide dressing assistance, but physical interactions between clothing and the human body can be complex and difficult to visually observe. We provide evidence that data-driven haptic perception can be used to infer relationships between clothing and the human body during robot-assisted dressing. We conducted a carefully controlled experiment with 12 human participants during which a robot pulled a hospital gown along the length of each person’s forearm 30 times. This representative task resulted in one of the following three outcomes: the hand missed the opening to the sleeve; the hand or forearm became caught on the sleeve; or the full forearm successfully entered the sleeve. We found that hidden Markov models (HMMs) using only forces measured at the robot’s end effector classified these outcomes with high accuracy. The HMMs’ performance generalized well to participants (98.61% accuracy) and velocities (98.61% accuracy) outside of the training data. They also performed well when we limited the force applied by the robot (95.8% accuracy with a 2N threshold), and could predict the outcome early in the process. Despite the lightweight hospital gown, HMMs that used forces in the direction of gravity substantially outperformed those that did not. The best performing HMMs used forces in the direction of motion and the direction of gravity.	en_US
dc.identifier.citation	A. Kapusta, W. Yu, T. Bhattacharjee, C. K. Liu, G. Turk and C. C. Kemp (2016). Data-Driven Haptic Perception for Robot-Assisted Dressing. 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), New York, NY, 2016, pp. 451-458.	en_US
dc.identifier.doi	10.1109/ROMAN.2016.7745158	en_US
dc.identifier.isbn	978-1-5090-3929-6
dc.identifier.uri	http://hdl.handle.net/1853/56461
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.publisher.original	Institute of Electrical and Electronics Engineers
dc.subject	Activity of daily living	en_US
dc.subject	Assisted living	en_US
dc.subject	Hidden Markov models	en_US
dc.subject	Human-robot interaction	en_US
dc.title	Data-Driven Haptic Perception for Robot-Assisted Dressing	en_US
dc.type	Text
dc.type.genre	Proceedings
dspace.entity.type	Publication
local.contributor.author	Turk, Greg
local.contributor.author	Kemp, Charles C.
local.contributor.corporatename	Rehabilitation Engineering Research Center on Technologies to Support Aging-in-Place for People with Long-Term Disabilities
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