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首页> 外文会议>International Conference on Pattern Recognition Applications and Methods >Measuring Physical Activity of Older Adults via Smartwatch and Stigmergic Receptive Fields
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Measuring Physical Activity of Older Adults via Smartwatch and Stigmergic Receptive Fields

机译：通过Smartwatch和Stigmercic接受领域测量老年人的身体活动

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摘要

Physical activity level (PAL) in older adults can enhance healthy aging, improve functional capacity, and prevent diseases. It is known that human annotations of PAL can be affected by subjectivity and inaccuracy. Recently developed smart devices can allow a non-invasive, analytic, and continuous gathering of physiological signals. We present an innovative computational system fed by signals of heartbeat rate, wrist motion and pedometer sensed by a smartwatch. More specifically, samples of each signal are aggregated by functional structures called trails. The trailing process is inspired by stigmergy, an insects' coordination mechanism, and is managed by computational units called stigmergic receptive fields (SRFs). SRFs, which compute the similarity between trails, are arranged in a stigmergic perceptron to detect a collection of micro-behaviours of the raw signal, called archetypes. A SRF is adaptive to subjects: its structural parameters are tuned by a differential evolution algorithm. SRFs are used in a multilayer architecture, providing further levels of processing to realize macro analyses in the application domain. As a result, the architecture provides a daily PAL, useful to detect behavioural shift indicating initial signs of disease or deviations in performance. As a proof of concept, the approach has been experimented on three subjects.
机译：老年人的身体活动水平（PAL）可以增强健康的老化，改善功能能力，并预防疾病。众所周知，PAL的人类注释可能受主观性和不准确的影响。最近开发的智能设备可以允许非侵入性，分析和生理信号的连续收集。我们提出了一种由SmartWatch感测的心跳​​速率，手腕运动和计步器的信号供给的创新计算系统。更具体地，每个信号的样本被称为路径的功能结构聚合。尾随过程受到昆虫的协调机制的灵感，并由称为耻辱感受的计算单元（SRF）来管理。计算路径之间的相似性的SRFS布置在柱塞的Perceptron中，以检测原始信号的微行为集合，称为原型。 SRF适用于受试者：其结构参数由差分演进算法调整。 SRFS用于多层体系结构，提供进一步的处理级别以实现应用程序域中的宏分析。结果，该架构提供了每日PAL，可用于检测表明疾病初始迹象或性能偏差的行为转变。作为概念证明，该方法已经在三个科目上进行了实验。

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