About: Acoustic Based Footstep Detection in Pervasive Healthcare

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #91424 Acoustic Based Footstep Detection in Pervasive Healthcare
sameAs	Acoustic Based Footstep Detection in Pervasive Healthcare
http://eprints.org/ontology/hasAccepted	Acoustic Based Footstep Detection in Pervasive Healthcare (PDF)
http://eprints.org/ontology/hasDocument	Acoustic Based Footstep Detection in Pervasive Healthcare (PDF) Acoustic Based Footstep Detection in Pervasive Healthcare (Other) Acoustic Based Footstep Detection in Pervasive Healthcare (Other) Acoustic Based Footstep Detection in Pervasive Healthcare (Other) Acoustic Based Footstep Detection in Pervasive Healthcare (Other) Acoustic Based Footstep Detection in Pervasive Healthcare (Other)
dc:hasVersion	Acoustic Based Footstep Detection in Pervasive Healthcare (PDF)
Title	Acoustic Based Footstep Detection in Pervasive Healthcare
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2021-12-09
Creator	S. Dasgupta D. Das C. Efstratiou K. Summoogum I. McLoughlin Palaniappan Ramaswamy
status	peer reviewed published
Publisher	IEEE
abstract	Passive detection of footsteps in domestic settings can allow the development of assistive technologies that can monitor mobility patterns of older adults in their home environment. Acoustic footstep detection is a promising approach for nonintrusive detection of footsteps. So far there has been limited work in developing robust acoustic footstep detection systems that can operate in noisy home environments. In this paper, we propose a novel application of the Attention based Recurrent Deep Neural Network to detect human footsteps in noisy overlapping audio streams. The model is trained on synthetic data which simulates the acoustic scene in a home environment. To evaluate performance, we reproduced two footstep detection models from literature and compared them using the newly developed Polyphonic Sound Detection Scores (PSDS). Our model achieved the highest PSDS and is close to the highest score achieved by generic indoor AED models in DCASE. The proposed system is designed to both detect and track footsteps within a home setting, and to enhance state-of-the-art digital health-care solutions for empowering older adults to live autonomously in their own homes.
Is Part Of	https://kar.kent.ac.uk/id/repository
Subject	R858 Computer applications to medicine. Medical informatics. Medical information technology
list of authors	https://kar.kent.ac.uk/id/eprint/91424#authors
presented at	43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #91424 Acoustic Based Footstep Detection in Pervasive Healthcare

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