de Arriba-Pérez Francisco, García-Méndez Silvia, González-Castaño Francisco J, Costa-Montenegro Enrique
Information Technologies Group, atlanTTic, School of Telecommunications Engineering, University of Vigo, Campus Lagoas-Marcosende, 36310 Vigo, Spain.
J Ambient Intell Humaniz Comput. 2022 Apr 29:1-16. doi: 10.1007/s12652-022-03849-2.
Previous researchers have proposed intelligent systems for therapeutic monitoring of cognitive impairments. However, most existing practical approaches for this purpose are based on manual tests. This raises issues such as excessive caretaking effort and the white-coat effect. To avoid these issues, we present an intelligent conversational system for entertaining elderly people with news of their interest that monitors cognitive impairment transparently. Automatic chatbot dialogue stages allow assessing content description skills and detecting cognitive impairment with Machine Learning algorithms. We create these dialogue flows automatically from updated news items using Natural Language Generation techniques. The system also infers the gold standard of the answers to the questions, so it can assess cognitive capabilities automatically by comparing these answers with the user responses. It employs a similarity metric with values in [0, 1], in increasing level of similarity. To evaluate the performance and usability of our approach, we have conducted field tests with a test group of 30 elderly people in the earliest stages of dementia, under the supervision of gerontologists. In the experiments, we have analysed the effect of stress and concentration in these users. Those without cognitive impairment performed up to five times better. In particular, the similarity metric varied between 0.03, for stressed and unfocused participants, and 0.36, for relaxed and focused users. Finally, we developed a Machine Learning algorithm based on textual analysis features for automatic cognitive impairment detection, which attained accuracy, F-measure and recall levels above 80%. We have thus validated the automatic approach to detect cognitive impairment in elderly people based on entertainment content. The results suggest that the solution has strong potential for long-term user-friendly therapeutic monitoring of elderly people.
先前的研究人员已经提出了用于认知障碍治疗监测的智能系统。然而，目前大多数用于此目的的实际方法都是基于手动测试。这引发了诸如过度的护理工作和白大褂效应等问题。为了避免这些问题，我们提出了一个智能对话系统，它能以老年人感兴趣的新闻来娱乐他们，同时透明地监测认知障碍。自动聊天机器人对话阶段允许使用机器学习算法评估内容描述技能并检测认知障碍。我们使用自然语言生成技术从更新的新闻项目中自动创建这些对话流程。该系统还能推断问题答案的黄金标准，因此它可以通过将这些答案与用户回复进行比较来自动评估认知能力。它采用了一个相似度度量，其值在[0, 1]范围内，相似度水平递增。为了评估我们方法的性能和可用性，我们在老年病专家的监督下，对30名处于痴呆症早期阶段的老年人测试组进行了现场测试。在实验中，我们分析了压力和注意力集中对这些用户的影响。没有认知障碍的用户表现要好多达五倍。特别是，相似度度量在压力大且注意力不集中的参与者为0.03，而放松且注意力集中的用户为0.36之间变化。最后，我们基于文本分析特征开发了一种用于自动认知障碍检测的机器学习算法，其准确率、F值和召回率均高于80%。因此，我们验证了基于娱乐内容检测老年人认知障碍的自动方法。结果表明，该解决方案在对老年人进行长期用户友好型治疗监测方面具有强大的潜力。
