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首页> 外文会议>2016 IEEE First Conference on Connected Health: Applications, Systems and Engineering Technologies >Automated Functional and Behavioral Health Assessment of Older Adults with Dementia
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Automated Functional and Behavioral Health Assessment of Older Adults with Dementia

机译：老年痴呆症患者的自动功能和行为健康评估

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摘要

Dementia is a clinical syndrome of cognitive deficits that involves both memory and functional impairments. While disruptions in cognition is a striking feature of dementia, it is also closely coupled with changes in functional and behavioral health of older adults. In this paper, we investigate the challenges of improving the automatic assessment of dementia, by better exploiting the emerging physiological sensors in conjunction with ambient sensors in a real field environment with IRB approval. We hypothesize that the cognitive health of older individuals can be estimated by tracking their daily activities and mental arousal states. We employ signal processing on wearable sensor data streams (e.g., Electrodermal Activity (EDA), Photoplethysmogram (PPG), accelerometer (ACC)) and machine learning algorithms to assess cognitive impairments and its correlation with functional health decline. To validate our approach, we quantify the score of machine learning, survey and observation based Activities of Daily Living (ADLs) and signal processing based mental arousal state, respectively for functional and behavioral health measures among 17 older adults living in a continuing care retirement community in Baltimore. We compare clinically observed scores with technology guided automated scores using both machine learning and statistical techniques.
机译：痴呆是一种认知缺陷的临床综合征，涉及记忆和功能障碍。认知障碍虽然是痴呆症的显着特征，但它也与老年人功能和行为健康的变化紧密相关。在本文中，我们通过在具有IRB批准的真实环境中更好地利用新兴的生理传感器和环境传感器，来研究改善痴呆症自动评估的挑战。我们假设可以通过跟踪老年人的日常活动和精神唤醒状态来估计其认知健康。我们对可穿戴式传感器数据流（例如，皮肤电活动（EDA），光电容积描记（PPG），加速度计（ACC））和机器学习算法进行信号处理，以评估认知障碍及其与功能健康下降的相关性。为了验证我们的方法，我们量化了基于机器学习，调查和观察的日常活动（ADL）和基于信号处理的精神唤醒状态的得分，分别针对居住在持续护理退休社区中的17位老年人的功能和行为健康措施在巴尔的摩。我们使用机器学习和统计技术将临床观察到的分数与技术指导的自动化分数进行比较。

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