GTM-Grup de recerca en Tecnologies Mèdia, La Salle-Universitat Ramon Llull, C/Quatre Camins, 30, 08022 Barcelona, Catalonia, Spain.
GRITS-Grup de Recerca en Internet Technologies & Storage, La Salle-Universitat Ramon Llull, C/Quatre Camins, 30, 08022 Barcelona, Catalonia, Spain.
Sensors (Basel). 2017 Apr 13;17(4):854. doi: 10.3390/s17040854.
The consistent growth in human life expectancy during the recent years has driven governments and private organizations to increase the efforts in caring for the eldest segment of the population. These institutions have built hospitals and retirement homes that have been rapidly overfilled, making their associated maintenance and operating costs prohibitive. The latest advances in technology and communications envisage new ways to monitor those people with special needs at their own home, increasing their quality of life in a cost-affordable way. The purpose of this paper is to present an Ambient Assisted Living (AAL) platform able to analyze, identify, and detect specific acoustic events happening in daily life environments, which enables the medic staff to remotely track the status of every patient in real-time. Additionally, this tele-care proposal is validated through a proof-of-concept experiment that takes benefit of the capabilities of the NVIDIA Graphical Processing Unit running on a Jetson TK1 board to locally detect acoustic events. Conducted experiments demonstrate the feasibility of this approach by reaching an overall accuracy of 82% when identifying a set of 14 indoor environment events related to the domestic surveillance and patients' behaviour monitoring field. Obtained results encourage practitioners to keep working in this direction, and enable health care providers to remotely track the status of their patients in real-time with non-invasive methods.
近年来，人类预期寿命的持续增长促使政府和私营组织加大力度照顾人口中年龄最大的群体。这些机构已经建立了医院和养老院，这些养老院迅速人满为患，使其相关的维护和运营成本变得过高。最新的技术和通信进步设想了新的方法来监测那些有特殊需求的人在自己家中的情况，以负担得起的成本提高他们的生活质量。本文的目的是介绍一个能够分析、识别和检测日常生活环境中特定声学事件的安闲辅助生活 (AAL) 平台，使医务人员能够实时远程跟踪每个患者的状态。此外，该远程护理方案通过利用 NVIDIA 图形处理单元在 Jetson TK1 板上运行的功能进行的概念验证实验得到验证，以在本地检测声学事件。进行的实验通过在识别与家庭监控和患者行为监测领域相关的 14 个室内环境事件时达到 82%的总体准确性，证明了该方法的可行性。获得的结果鼓励从业者朝着这个方向继续努力，并使医疗保健提供者能够通过非侵入性方法实时远程跟踪患者的状态。
