The rapid increase in the senior population is posing serious challenges to national healthcare systems. Hence, innovative tools are needed to early detect health issues, including cognitive decline. Several clinical studies show that it is possible to identify cognitive impairment based on the locomotion patterns of the elderly. In this work, we investigate the use of sensor data and deep learning to recognize those patterns in instrumented smart-homes. In order to get rid of the noise introduced by indoor constraints and activity execution, we introduce novel visual feature extraction methods for locomotion data. Our solution relies on locomotion trace segmentation, image-based extraction of salient features from locomotion segments, and vision-based deep learning. We carried out extensive experiments with a large dataset acquired in a smart-home test bed from 153 seniors, including people with cognitive diseases. Results show that our system can accurately recognize the cognitive status of the senior, reaching a macro-\(F_1\) score of 0.873 for the three categories that we target: cognitive health, mild cognitive impairment, and dementia. Moreover, an experimental comparison shows that our system outperforms state-of-the-art methods.

老年人口的迅速增加给国家医疗保健系统带来了严峻的挑战。因此，需要创新工具来及早发现健康问题，包括认知能力下降。多项临床研究表明，可以根据老年人的运动模式识别认知障碍。在这项工作中，我们研究使用传感器数据和深度学习来识别仪表化智能家居中的这些模式。为了摆脱室内约束和活动执行引入的噪声，我们为运动数据引入了新颖的视觉特征提取方法。我们的解决方案依赖于运动轨迹分割、基于图像的运动片段显着特征提取以及基于视觉的深度学习。我们使用从 153 名老年人（包括患有认知疾病的人）获得的大型数据集进行了广泛的实验。结果表明，我们的系统可以准确识别老年人的认知状态，达到宏观我们所针对的三个类别的\(F_1\)得分为 0.873：认知健康、轻度认知障碍和痴呆。此外，实验比较表明我们的系统优于最先进的方法。