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Automatic classification of gait patterns using a smart rollator and the BOSS model

Author
Ojeda, M.; Cortes, A.; Bejar, J.; Cortes, U.
Type of activity
Presentation of work at congresses
Name of edition
11th PErvasive Technologies Related to Assistive Environments Conference
Date of publication
2018
Presentation's date
2018-06-26
Book of congress proceedings
EuroMPI 2018: proceedings of the 25th European MPI Users’ Group Meeting: Barcelona, Spain: September 23-26, 2018
First page
384
Last page
390
Publisher
Association for Computing Machinery (ACM)
DOI
https://doi.org/10.1145/3197768.3201575 Open in new window
Project funding
Personalized and adaptive rehabilitation in post-stroke treatments: the i-Walker
Repository
http://hdl.handle.net/2117/121595 Open in new window
URL
https://dl.acm.org/citation.cfm?id=3201575&dl=ACM&coll=DL Open in new window
Abstract
Nowadays, the risk of falling in older adults is a major concern due to the severe consequences it brings to socio-economic and public health systems. Some pathologies cause mobility problems in the aged population, leading them to fall and, thus, reduce their autonomy. Other implications of ageing involve having different gait patterns and walking speed. In this paper, a non-invasive framework is proposed to study gait in elder people using data collected by a smart rollator, the i-Walker. The ...
Citation
Ojeda, M., Cortés , A., Béjar, J., Cortés, U. Automatic classification of gait patterns using a smart rollator and the BOSS model. A: PErvasive Technologies Related to Assistive Environments Conference. "PETRA '18 Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference". New York: Association for Computing Machinery (ACM), 2018, p. 384-390.
Keywords
Assistive technologies, Assistive technology, Automatic classification, Bayesian clustering, Feature extraction methods, Gait analysis, Groups of interests, Health risks, Learning systems, Machine learning, Public health systems, Spectral embedding, Time series analysis, Time series clustering, Time series clustering Gait analysis
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
KEMLG - Knowledge Engineering and Machine Learning Group

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