Document - Detecting User's Behavior Shift with Sensorized Shoes and Stigmergic Perceptrons

2019

Conference article Open Access

Detecting User's Behavior Shift with Sensorized Shoes and Stigmergic Perceptrons

Barsocchi P., Carbonaro N., Cimino M. G. C. A., Rosa D. L., Palumbo F., Tognetti A., Vaglini G.

Long-term monitoring AAL Artificial Receptive Field Stigmergic Perceptron Well-being assessment

As populations become increasingly aged, health monitoring has gained increasing importance. Recent advances in engineering of sensing, processing and artificial learning, make the development of non-invasive systems able to observe changes over time possible. In this context, the Ki-Foot project aims at developing a sensorized shoe and a machine learning architecture based on computational stigmergy to detect small variations in subjects gait and to learn and detect users behavior shift. This paper outlines the challenges in the field and summarizes the proposed approach. The machine learning architecture has been developed and publicly released after early experimentation, in order to foster its application on real environments.

Source: 2019 IEEE 23rd International Symposium on Consumer Technologies (ISCT), pp. 265–268, Ancona, Italy, 19-21/6/2019

Publisher: IEEE, New York, USA

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@inproceedings{oai:it.cnr:prodotti:416309,
	title = {Detecting User's Behavior Shift with Sensorized Shoes and Stigmergic Perceptrons},
	author = {Barsocchi P. and Carbonaro N. and Cimino M. G. C. A. and Rosa D. L. and Palumbo F. and Tognetti A. and Vaglini G.},
	publisher = {IEEE, New York, USA},
	doi = {10.1109/isce.2019.8901007},
	booktitle = {2019 IEEE 23rd International Symposium on Consumer Technologies (ISCT), pp. 265–268, Ancona, Italy, 19-21/6/2019},
	year = {2019}
}