MAISON -老年人多模态人工智能传感器平台

A. Abedi, Faranak Dayyani, Charlene H. Chu, Shehroz S. Khan
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引用次数: 1

摘要

全球人口老龄化要求需要合适的工具,使老年人能够更大的独立性和在家养老的能力,并协助医护人员。通过建立预测模型来帮助医护人员监测和分析老年人的行为、功能和心理数据,实现这一目标是可行的。为了建立这样的模型,通常需要大量的多模态传感器数据。在本文中,我们提出了MAISON,这是一个可扩展的基于云的商用智能设备平台,能够从生活在自己家中的老年人和患者那里收集所需的多模态传感器数据。MAISON平台是新颖的,因为它能够收集比现有平台更多种类的数据模式,以及它的新功能,可以无缝地收集数据,并且对于可能不懂数字的老年人来说很容易使用。我们用两名从大型康复中心出院回家的老年人来证明MAISON平台的可行性。结果表明,MAISON平台能够在云中收集和存储传感器数据,而不会出现功能故障或性能下降。本文还将讨论平台开发和老年人家庭数据收集过程中面临的挑战。MAISON是一个新颖的平台,旨在收集多模式数据,促进预测模型的发展,以检测关键的健康指标,包括社会孤立、抑郁和功能衰退,并且在社区老年人中使用是可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MAISON - Multimodal AI-based Sensor platform for Older Individuals
There is a global aging population requiring the need for the right tools that can enable older adults' greater independence and the ability to age at home, as well as assist healthcare workers. It is feasible to achieve this objective by building predictive models that assist healthcare workers in monitoring and analyzing older adults' behavioral, functional, and psychological data. To develop such models, a large amount of multimodal sensor data is typically required. In this paper, we propose MAISON, a scalable cloud-based platform of commercially available smart devices capable of collecting desired multimodal sensor data from older adults and patients living in their own homes. The MAISON platform is novel due to its ability to collect a greater variety of data modalities than the existing platforms, as well as its new features that result in seamless data collection and ease of use for older adults who may not be digitally literate. We demonstrated the feasibility of the MAISON platform with two older adults discharged home from a large rehabilitation center. The results indicate that the MAISON platform was able to collect and store sensor data in a cloud without functional glitches or performance degradation. This paper will also discuss the challenges faced during the development of the platform and data collection in the homes of the older adults. MAISON is a novel platform designed to collect multimodal data and facilitate the development of predictive models for detecting key health indicators, including social isolation, depression, and functional decline, and is feasible to use with older adults in the community.
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