Intelligent environments may improve the independence and quality of life of persons with mild cognitive impairment (MCI) or Alzheimer’s disease (AD) through their ability to automatically provide assistance or guidance. In order to deploy these systems in this category of the population, it is necessary to be able to carry out validation and experiments to improve efficiency, safety, user experience and reduce installation costs. Unfortunately, this type of experiment can be difficult to perform because of the difficulty in recruiting candidates and accessing adequate intelligent environments. These problems could be partially offset with simulators. These tools can be used to simulate the behavior of an intelligent environment and its occupants in order to generate data, or to observe and evaluate their behavior. However, to design systems for populations suffering from MCI or AD, it is necessary that the simulator be able to emulate the behavior of these persons. In this paper, two approaches to simulate and generate sequences of actions containing errors usually committed by persons with this type of disease are proposed. Those approaches aim to be simple to use and both are based on the use of behavior trees. The first one consists in adding nodes to a behavior tree to simulate errors with their specific probabilities. The second approach consists in defining an interval to bind the number of errors that can be inserted through the error injection algorithm. We also present the results of the experiments carried out to evaluate these approaches. For the first experiment, several simulations were conducted and were recorded in videos. These videos were analyzed by specialists in cognitive disorders who diagnosed the avatar of these videos. The second experiment aimed at comparing the two approaches together. To do so, several action sequences were generated. The results show that our model is able to generate healthy, MCI and Alzheimer’s behaviors. The results also show that the second approach facilitates the generation of a desired number of errors.

中文翻译：

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为轻度认知障碍或阿尔茨海默氏症患者的行为建模以进行智能环境模拟

智能环境可以通过自动提供帮助或指导的能力来提高轻度认知障碍 (MCI) 或阿尔茨海默病 (AD) 患者的独立性和生活质量。为了在这一类人群中部署这些系统，需要能够进行验证和实验，以提高效率、安全性、用户体验并降低安装成本。不幸的是，由于难以招募候选人和访问足够的智能环境，这种类型的实验可能难以执行。这些问题可以用模拟器部分抵消。这些工具可用于模拟智能环境及其居住者的行为，以生成数据，或观察和评估他们的行为。然而，为了为患有 MCI 或 AD 的人群设计系统，模拟器必须能够模拟这些人的行为。在本文中，提出了两种方法来模拟和生成包含此类疾病患者通常会犯的错误的动作序列。这些方法旨在简单易用，并且都基于行为树的使用。第一个包括向行为树添加节点以模拟具有特定概率的错误。第二种方法包括定义一个区间来绑定可以通过错误注入算法插入的错误数量。我们还介绍了为评估这些方法而进行的实验结果。对于第一个实验，进行了多次模拟并记录在视频中。这些视频由诊断出这些视频头像的认知障碍专家进行分析。第二个实验旨在比较这两种方法。为此，生成了几个动作序列。结果表明，我们的模型能够生成健康、MCI 和阿尔茨海默氏症的行为。结果还表明，第二种方法有助于产生所需数量的错误。