Our aging society claims for innovative tools to early detect symptoms of cognitive decline. Several research efforts are being made to exploit sensorized smart-homes and artificial intelligence (AI) methods to detect a decline of the cognitive functions of the elderly in order to promptly alert practitioners. Even though those tools may provide accurate predictions, they currently provide limited support to clinicians in making a diagnosis. Indeed, most AI systems do not provide any explanation of the reason why a given prediction was computed. Other systems are based on a set of rules that are easy to interpret by a human. However, those rule-based systems can cope with a limited number of abnormal situations, and are not flexible enough to adapt to different users and contextual situations. In this paper, we tackle this challenging problem by proposing a flexible AI system to recognize early symptoms of cognitive decline in smart-homes, which is able to explain the reason of predictions at a fine-grained level. Our method relies on well known clinical indicators that consider subtle and overt behavioral anomalies, as well as spatial disorientation and wandering behaviors. In order to adapt to different individuals and situations, anomalies are recognized using a collaborative approach. We experimented our approach with a large set of real world subjects, including people with MCI and people with dementia. We also implemented a dashboard to allow clinicians to inspect anomalies together with the explanations of predictions. Results show that our system’s predictions are significantly correlated to the person’s actual diagnosis. Moreover, a preliminary user study with clinicians suggests that the explanation capabilities of our system are useful to improve the task performance and to increase trust. To the best of our knowledge, this is the first work that explores data-driven explainable AI for supporting the diagnosis of cognitive decline.

我们的老龄化社会要求采用创新工具来及早发现认知能力下降的症状。为了迅速提醒从业人员，人们正在开展一些研究工作，以开发传感器化的智能家居和人工智能（AI）方法来检测老年人的认知功能下降。即使那些工具可以提供准确的预测，但它们目前对临床医生进行诊断的支持有限。实际上，大多数AI系统都没有提供任何计算给定预测的原因的解释。其他系统基于一组易于被人解释的规则。但是，那些基于规则的系统只能应付有限数量的异常情况，并且不够灵活，无法适应不同的用户和上下文情况。在本文中，我们提出了一种灵活的AI系统来识别智能家居中认知能力下降的早期症状，从而解决了这一具有挑战性的问题，该系统可以在细粒度的层次上解释预测的原因。我们的方法依赖于众所周知的临床指标，这些指标考虑了细微和明显的行为异常，以及空间迷失方向和游荡行为。为了适应不同的个人和情况，使用协作方法来识别异常。我们对许多现实世界的对象（包括MCI患者和痴呆症患者）进行了实验。我们还实施了一个仪表板，以允许临床医生检查异常以及对预测的解释。结果表明，我们系统的预测与患者的实际诊断显着相关。此外，与临床医生进行的初步用户研究表明，我们系统的解释功能可用于改善任务绩效和增加信任度。据我们所知，这是探索支持数据驱动的可解释性AI诊断认知能力下降的第一项工作。