用可穿戴设备+AI预测老年人认知能力，连续监测更高效。
Predicting Cognitive Assessment Scores in Older Adults with Cognitive Impairment Using Wearable Sensors
- 用可穿戴设备采集生理信号，结合机器学习建模预测认知分数。
- 预测准确率相关系数达0.73-0.82，误差仅0.14-0.16分。
- 不同认知功能对应不同传感器组合，适合远程老年认知监测。
本研究利用人工智能评估轻度认知障碍或轻度痴呆老年人的认知功能，基于可穿戴设备提供的生理数据。传统认知筛查耗时且仅捕捉短暂状态，而可穿戴传感器可连续监测生理信号。研究对23名老年人在完成三项NIH Toolbox Cognitive Battery测试（工作记忆、加工速度、注意力）时的生理信号进行记录，使用Empatica EmbracePlus设备测量血容量脉搏、皮肤电导、温度和运动。通过小波分析与分段法提取统计特征，采用监督学习模型并以交叉验证、留出测试和自助法验证预测效果。结果显示，模型表现优异，斯皮尔曼相关系数为0.73–0.82，平均绝对误差为0.14–0.16，显著优于均值预测基准。各传感器作用各异：心率相关信号结合运动与温度最有助于预测工作记忆；运动与皮肤电导组合对加工速度预测最优；心率与皮肤电导协同表现最佳于注意力预测。研究证明，结合人工智能与特征工程的可穿戴传感系统可非侵入式持续追踪特定认知功能，尤其适用于小样本场景，具备支持远程评估与临床干预的潜力。
原文摘要 · Abstract (English)
Background and Objectives: This paper focuses on using AI to assess the cognitive function of older adults with mild cognitive impairment or mild dementia using physiological data provided by a wearable device. Cognitive screening tools are disruptive, time-consuming, and only capture brief snapshots of activity. Wearable sensors offer an attractive alternative by continuously monitoring physiological signals. This study investigated whether physiological data can accurately predict scores on established cognitive tests. Research Design and Methods: We recorded physiological signals from 23 older adults completing three NIH Toolbox Cognitive Battery tests, which assess working memory, processing speed, and attention. The Empatica EmbracePlus, a wearable device, measured blood volume pulse, skin conductance, temperature, and movement. Statistical features were extracted using wavelet-based and segmentation methods. We then applied supervised learning and validated predictions via cross-validation, hold-out testing, and bootstrapping. Results: Our models showed strong performance with Spearman's ρof 0.73-0.82 and mean absolute errors of 0.14-0.16, significantly outperforming a naive mean predictor. Sensor roles varied: heart-related signals combined with movement and temperature best predicted working memory, movement paired with skin conductance was most informative for processing speed, and heart in tandem with skin conductance worked best for attention. Discussion and Implications: These findings suggest that wearable sensors paired with AI tools such as supervised learning and feature engineering can noninvasively track specific cognitive functions in older adults, enabling continuous monitoring. Our study demonstrates how AI can be leveraged when the data sample is small. This approach may support remote assessments and facilitate clinical interventions.
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