Persim -模拟人类活动在无处不在的空间

A. Helal, Jaewoong Lee, S. Hossain, Eunju Kim, H. Hagras, D. Cook
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引用次数: 60

摘要

活动识别研究在很大程度上依赖于测试数据来验证活动识别算法的建模技术和性能。但是从实际部署中获取数据既昂贵又耗时。即使成本不是问题,对人类受试者使用的监管限制也禁止收集可以在所有情况下测试所有场景的广泛数据集。为了加快人体活动识别的研究,需要一种功能强大且可验证的仿真工具。我们提出了Persim,一个事件驱动的人类活动模拟器在无处不在的空间。Persim能够捕捉空间、传感器、行为(活动)及其相互关系的元素。我们将重点介绍Persim的五个主要用例,包括数据集合成、现有数据集的重用和扩展、数据和模拟项目的共享以及数据验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Persim - Simulator for Human Activities in Pervasive Spaces
Activity recognition research relies heavily on test data to verify the modeling technique and the performance of the activity recognition algorithm. But data from real deployments are expensive and time consuming to obtain. And even if cost is not an issue, regulatory limitations on the use of human subjects prohibit the collection of extensive datasets that can test all scenarios, under all circumstances. A powerful and verifiable simulation tool is needed to accelerate research on human activity recognition. We present Persim, an event driven simulator of human activities in pervasive spaces. Persim is capable of capturing elements of space, sensors, behaviors (activities), and their inter-relationships. We focus on presenting the five main use cases for Persim addressing dataset synthesis, reuse and extension of existing datasets, sharing of data and simulation projects, as well as data validation.
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