A quantitative analysis of dressing dynamics for robotic dressing assistance
Open accessAbstract
Assistive robots have a great potential to address issues related to an ageing population and an increased demand for caregiving. Successful deployment of robots working in close proximity with people requires consideration of both safety and human-robot interaction. One of the established activities of daily living where robots could play an assistive role is dressing. Using the correct force profile for robot control will be essential in this application of human-robot interaction requiring careful exploration of factors related to the user’s pose and the type of garments involved. In this paper a Baxter robot was used to dress a jacket onto a mannequin and human participants considering several combinations of user pose and clothing type (base layers), whilst recording dynamic data from the robot, a load cell and an IMU. We also report on suitability of these sensors for identifying dressing errors, e.g. fabric snagging. Data was analyzed by comparing the overlap of confidence intervals to determine sensitivity to dressing. We expand the analysis to include classification techniques such as decision tree and support vector machines using k-fold cross-validation. The 6-axis load cell successfully discriminated between clothing types with predictive model accuracies between 72-97%. Used independently, the IMU and Baxter sensors were insufficient to discriminate garment types with the IMU showing 40-72% accuracy, but when used in combination this pair of sensors achieved an accuracy similar to the more expensive load cell (98%). When observing dressing errors (snagging) Baxter’s sensors and the IMU data demonstrated poor sensitivity but applying machine learning methods resulted in model with high predicative accuracy and low false negative rates (≤5%). The results show that the load cell could be used independently for this application with good accuracy but a combination of the lower cost sensors could also be used without a significant loss in precision which will be a key element in the robot control architecture for safe human-robot interaction.
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The system needs to detect success and different error states.
“In addition, the system needs to detect success and different error states [298,301,309,315,316,317].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [316]
The Methodology system setup section says the sensors are intended to detect dressing errors, and Predictive Models for error Detection distinguishes normal dressing from a snagging error. The paper does not establish detection of successful completion or distinguish among different error states; it tests one simulated error type. (LLM, conf=0.85)
Experimental — accuracy not yet measured
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Cites
Smoothing and Differentiation of Data by Simplified Least Squares Procedures.
Savitzky, A., & Golay, M. J. E. (1964) · Analytical Chemistry · doi:10.1021/ac60214a047 · Cited by 21,904 · see citing works
Least Squares Support Vector Machine Classifiers
Suykens, J., & Vandewalle, J. (1999) · Neural Processing Letters · doi:10.1023/a:1018628609742 · Cited by 9,481 · see citing works
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance
Seiffert, C., Khoshgoftaar, T. M., Hulse, J. V., & Napolitano, A. (2009) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2009.2029559 · Cited by 1,872 · see citing works
On judging the significance of differences by examining the overlap between confidence intervals.
Schenker, N., & Gentleman, J. F. (2001) · The American Statistician · doi:10.1198/000313001317097960 · Cited by 1,218 · see citing works
Shao, Y., & Lunetta, R. S. (2012) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2012.04.001 · Cited by 638 · see citing works
In vivo friction properties of human skin
Zhang, M., & Mak, A. F. T. (1999) · Prosthetics and Orthotics International · doi:10.3109/03093649909071625 · Cited by 346 · see citing works
When One Depends on the Other: Reporting of Interaction in Case-Control and Cohort Studies
Knol, M. J., Egger, M., Scott, P., Geerlings, M. I., & Vandenbroucke, J. P. (2009) · Epidemiology · doi:10.1097/ede.0b013e31818f6651 · Cited by 113 · see citing works
Reinforcement learning of clothing assistance with a dual-arm robot
Tamei, T., Matsubara, T., Rai, A., & Shibata, T. (2011) · 2011 11th IEEE-RAS International Conference on Humanoid Robots · doi:10.1109/humanoids.2011.6100915 · Cited by 103 · see citing works
Iterative path optimisation for personalised dressing assistance using vision and force information
Gao, Y., Chang, H. J., & Demiris, Y. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759647 · Cited by 78 · see citing works
Lee, M., & Chang, T. (2010) · International Journal of Artificial Intelligence & Applications · doi:10.5121/ijaia.2010.1303 · Cited by 73 · see citing works
User modelling for personalised dressing assistance by humanoid robots
Gao, Y., Chang, H. J., & Demiris, Y. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353617 · Cited by 71 · see citing works
Bathing care assistance with robot suit HAL
Satoh, H., Kawabata, T., & Sankai, Y. (2009) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2009.5420697 · Cited by 56 · see citing works
Managing Activity Difficulties at Home: A Survey of Medicare Beneficiaries
Dudgeon, B. J., Hoffman, J. M., Ciol, M. A., Shumway‐Cook, A., Yorkston, K. M., & Chan, L. E. (2008) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2007.11.038 · Cited by 51 · see citing works
Reactive navigation of an intelligent robotic walking aid
Graf, B. (2001) · doi:10.1109/roman.2001.981929 · Cited by 50 · see citing works
Data-driven haptic perception for robot-assisted dressing
Kapusta, A. S., Yu, W., Bhattacharjee, T., Liu, C. K., Turk, G., & Kemp, C. C. (2016) · IEEE RO-MAN · doi:10.1109/roman.2016.7745158 · Cited by 47 · see citing works
Bottom dressing by a life-sized humanoid robot provided failure detection and recovery functions
Yamazaki, K., Oya, R., Nagahama, K., Okada, K., & Inaba, M. (2014) · 2014 IEEE/SICE International Symposium on System Integration · doi:10.1109/sii.2014.7028101 · Cited by 42 · see citing works
Reinforcement learning of a motor skill for wearing a T-shirt using topology coordinates
Matsubara, T., Shinohara, D., & Kidode, M. (2013) · Advanced Robotics · doi:10.1080/01691864.2013.777012 · Cited by 41 · see citing works
An assistive robot to support dressing - strategies for planning and error handling
Chance, G., Camilleri, A., Winstone, B., Caleb-Solly, P., & Dogramadzi, S. (2016) · 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob.2016.7523721 · Cited by 34 · see citing works
Self-citationSit-to-stand assistance with a balancing mobile robot
Shomin, M., Forlizzi, J., & Hollis, R. L. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139727 · Cited by 30 · see citing works
Supervisory teleoperation with online learning and optimal control
Havoutis, I., & Calinon, S. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989183 · Cited by 28 · see citing works
Koganti, N., Tamei, T., Matsubara, T., & Shibata, T. (2013) · Proceedings of Conference on Advances In Robotics · doi:10.1145/2506095.2506146 · Cited by 21 · see citing works
Experimental verification of failure of Amontons' law in polymeric textiles
Ramkumar, S., Rajanala, R., Parameswaran, S., et al. (2004) · Journal of Applied Polymer Science · doi:10.1002/app.13566 · Cited by 15 · see citing works
Assessment of frictional properties of some woven fabrics
Kothari, V. K., & Gangal, M. K. (1994) · Cited by 13 · see citing works
Mutlu, B., Bartneck, C., der Ham, J. V., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-25504-5 · Cited by 13 · see citing works
Prescott, T. J., Epton, T., Evers, V., McKee, K., Webb, T. L., Benyon, D., Conran, S. O., Strand, R., Buning, M. D. C., Verschure, P. F. M. J., et al. (2012) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · Cited by 10 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Modeling, learning, perception, and control methods for deformable object manipulation
Yin, H., Varava, A., & Kragic, D. (2021) · Science Robotics · doi:10.1126/scirobotics.abd8803 · Cited by 299 · see citing works
Probabilistic real-time user posture tracking for personalized robot-assisted dressing
Zhang, F., Cully, A., & Demiris, Y. (2019) · IEEE Transactions on Robotics · doi:10.1109/tro.2019.2904461 · Cited by 70 · see citing works
Learning to collaborate from simulation for robot-assisted dressing
Clegg, A., Erickson, Z., Grady, P., Turk, G., Kemp, C. C., & Liu, C. K. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2972852 · Cited by 62 · see citing works
A framework for robotic clothing assistance by imitation learning
Joshi, R. P., Koganti, N., & Shibata, T. (2019) · Advanced Robotics · doi:10.1080/01691864.2019.1636715 · Cited by 50 · see citing works
Kadi, H. A., & Terzić, K. (2023) · Sensors · doi:10.3390/s23052389 · Cited by 19 · see citing works
- Authors
- Chance, G., Jevtić, A., Caleb-Solly, P., & Dogramadzi, S.
- Affiliations
- University of the West of England (GB)
- Institut de Robòtica i Informàtica Industrial (ES)
- Corresponding author
- Chance, Greg
- Venue
- Frontiers in Robotics and AI
- Publisher
- Frontiers Media SA
- Published
- 2017-05-16
- Volume
- 4
- ISSN
- 2296-9144 (ISSN-L)
- OpenAlex
- W2615693312
- Cited by
- 34
- References
- 25
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 2 · 2023: 2 · 2022: 4 · 2021: 6 · 2020: 3 · 2019: 4 · 2018: 3 · 2017: 1
- FWCI
- 1.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Robotics and AI (publishedVersion, cc-by)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (acceptedVersion, other-oa)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, cc-by-nc-nd)
- UWE Research Repository (UWE Bristol) (submittedVersion, cc-by)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, public-domain)
- Funders
- Engineering and Physical Sciences Research Council (EP/N021703/1)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
- Keywords
- assistive robots, human-robot interaction, inertial measurement unit, Baxter robot, activities of daily living, sensor fusion
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