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首页> 外文会议>2019 IEEE International Symposium on Measurements amp; Networking >Assistive sensor-based technology driven self-management for building resilience among people with early stage cognitive impairment
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Assistive sensor-based technology driven self-management for building resilience among people with early stage cognitive impairment

机译：基于辅助传感器的技术驱动的自我管理，可在早期认知障碍患者中增强适应力

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摘要

This paper reports the technologies and workplan of the AAL RESILIEN-T project. Focused on assistive technologies, RESILIEN-T aims to improve, through self-management, the autonomy, participation in social life, and skills, of older Persons with Cognitive Impairment (PwCI) who are too often considered as “objects” of research, rather than “partners”. The study investigates existing ICT solutions to improve the self-management ability of PwCl at different stages of cognitive impairment. Sensors, devices and apps to reduce the progression of the disease are analyzed. To increase sensor capability, innovative data management, i.e. Artificial Intelligence and Machine Learning algorithms, are considered to extract significant information from the data and optimize the sensor network. Moreover, approaches to involve end-users in the development are also investigated to enhance the final outputs. The study proposes a modular and integrated platform for PwCI to self-manage various activities including nutrition, physical activities, social life, cognitive training. The choice of offering an open API to integrate wearable devices and lifestyle monitoring systems from different suppliers makes available a customable and modular product. Considering that functional decline is part of the normal aging process, it might be challenging to individuate three levels of modular architecture to increase the accuracy of the monitoring with the decline of the cognitive capabilities.
机译：本文报告了AAL RESILIEN-T项目的技术和工作计划。 RESILIEN-T专注于辅助技术，旨在通过自我管理来改善通常被认为是研究“对象”的老年人认知障碍（PwCI）的自主权，参与社会生活和技能的能力。比“伙伴”。该研究调查了现有的ICT解决方案，以提高认知障碍不同阶段中PwCl的自我管理能力。分析了减少疾病进展的传感器，设备和应用程序。为了提高传感器的功能，创新的数据管理（即人工智能和机器学习算法）被认为可以从数据中提取重要信息并优化传感器网络。此外，还研究了使最终用户参与开发的方法，以提高最终产出。该研究为普华永道提出了一个模块化和集成的平台，以自我管理各种活动，包括营养，体育活动，社交生活，认知训练。提供开放的API来集成可穿戴设备和来自不同供应商的生活方式监控系统的选择可以提供可定制的模块化产品。考虑到功能下降是正常老化过程的一部分，可能难以区分模块化架构的三个级别，以随着认知能力的下降提高监视的准确性。

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