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Griffiths, Naomi and Chin, Jeannette 
ORCID: https://orcid.org/0000-0002-9398-5579
(2017)
Towards Unobtrusive Ambient Sound Monitoring for Smart and Assisted Environments.
In: 2016 8th Computer Science and Electronic Engineering (CEEC), 2016-09-28 - 2016-09-30.
Abstract
This paper proposes an implementation of a wireless sensor network as part of the Internet of Things using a network of distributed Raspberry Pi computers transmitting data over a ZigBee radio-mesh. The pervasive nature of noise in the modern environment makes this an ideal factor for monitoring unobtrusively in the Smart Environment. Noise monitoring using distributed Raspberry Pi computers has already been established in several other studies, but rarely in a domestic setting. The goal of this study is to evaluate the potential for using off-the-shelf, low-cost components to develop a wireless sensor network for unobtrusive sound monitoring in a domestic environment, where anomalous readings trigger alerts. The study investigates the transferability of the prototype into assisted living to enable seniors to live independently in their own homes. Assisted ambient living in a smart environment can provide support for both the elderly person and their carers through the provision of relevant data in a clear and easily accessible manner using cross-platform applications. Data can be accessed actively through an interface or passively when alerts are triggered.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | array(0x7feaf0e42058) |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory Faculty of Science > Research Groups > Interactive Graphics and Audio Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 27 Jun 2019 07:30 |
| Last Modified: | 24 Sep 2024 07:22 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/71560 |
| DOI: | 10.1109/CEEC.2016.7835882 |
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