课题基金 / 基金详情

Sounds Asleep - Towards better sleep quality with biometric noise metering and mediation

Sounds Asleep - Towards better sleep quality with biometric noise metering and mediation
睡着的声音 - 通过生物识别噪音测量和调解提高睡眠质量
批准号:
2163424
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2019
资助国家:
英国
项目状态:
已结题
起止时间:
2019 至 --

项目摘要

项目成果

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中文摘要
翻译
噪音会对健康和福祉产生重大影响,包括心血管健康,睡眠中断,工作认知和累积听力损伤。英国的许多人将他们的环境描述为过度嘈杂,相当大比例的英国人口对他们的听觉环境不满意(约30%),并且家庭环境中的睡眠中断已被证明会影响身心健康。非专业观众不易理解噪音措施,因此往往不鼓励公众积极采用。新的指标可能有助于人们处理噪音和睡眠中断。负担得起的,舒适的生物生理传感器的进步提出了一个独特的机会,探索现有的声学测量,自我报告的反应,如烦恼,和生物生理措施之间的关系,创造新的,统一的指标噪声measurement.This项目也将代表第一步的可能性,使用生物反馈,以促进噪声掩蔽使用自动音景生成。个体自适应声景掩蔽可以有效地调解噪声的影响-用户将最佳地保持在睡眠质量状态,而系统自主响应。机器学习可以提供一种方法,将生物反馈的复杂组合用作生成声景的有用控制信号。这种技术需要大量的数据,而大众市场的可穿戴设备现在已经达到了可以促进这种实验的成熟水平。
英文摘要
Noise can have a significant impact on health and wellbeing, including cardiovascular health, sleep disruption, working cognition, and cumulative hearing damage. Many people in the UK describe their environment as excessively noisy, a significant proportion of the UK population is dissatisfied with their auditory environment (~30%) and sleep disruption in the home environment has been shown to affect both mental and physical health. Noise measures are not readily interpreted by non-specialist audiences, and therefore tend not to encourage proactive adoption by the public. New metrics may help to empower people dealing with noise and interrupted sleep. Advances in affordable, comfortable biophysiological sensors suggest a unique opportunity to explore the relationship between existing acoustic measurement, self-reported responses such as annoyance, and biophysiological measures, creating new, consolidated metrics for noise measurement.This project will also represent the first step towards the possibility of using biofeedback to facilitate noise masking using automated soundscape generation. Individually-adaptive soundscape masking may be effective in mediating the impact of noise - the user would optimally remain in a quality state of sleep while the system responds autonomously. Machine learning can provide a way to use complex combinations of biofeedback as useful control signals for soundscape generation. Such techniques require a large amount of data and mass market wearables are now reaching a level of maturity which can facilitate such experiments.
期刊论文(2)
专著(0)
科研奖励(0)
会议论文
Soundscape Evaluation In The Virtual Reality: Tools for the creation of soundscape studies
虚拟现实中的声景评估:创建声景研究的工具
DOI: --
发表时间: 2022
期刊:
影响因子: --
作者: [Durbridge S]
通讯作者: Durbridge S
Assessment of soundscapes using self-report and physiological measures
使用自我报告和生理测量来评估声景
DOI: 10.1051/aacus/2022059
发表时间: 2023
期刊: Acta Acustica
影响因子: 1.4
作者: [Durbridge S]
通讯作者: Durbridge S
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