Rapid population aging and the availability of sensors and intelligent objects motivate the development of healthcare systems; these systems, in turn, meet the needs of older adults by supporting them to accomplish their day-to-day activities. Collecting information regarding older adults daily activity potentially helps to detect abnormal behavior. Anomaly detection can subsequently be combined with real-time, continuous and personalized interventions to help older adults actively enjoy a healthy lifestyle. This paper introduces a system that uses a novel approach to generate personalized health feedback. The proposed system models user’s daily behavior in order to detect anomalous behaviors and strategically generates interventions to encourage behaviors conducive to a healthier lifestyle. The system uses a Mamdani-type fuzzy rule-based component to predict the level of intervention needed for each detected anomaly and a sequential decision-making algorithm, Contextual Multi-armed Bandit, to generate suggestions to minimize anomalous behavior. We describe the system’s architecture in detail and we provide example implementations for the anomaly detection and corresponding health feedback.

中文翻译：

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老年人的个性化实时异常检测和健康反馈

人口的迅速老龄化以及传感器和智能对象的可用性推动了医疗保健系统的发展；这些系统通过支持老年人完成其日常活动来满足老年人的需求。收集有关老年人日常活动的信息可能有助于发现异常行为。随后可以将异常检测与实时，连续和个性化的干预措施相结合，以帮助老年人积极享受健康的生活方式。本文介绍了一种使用新颖方法生成个性化健康反馈的系统。所提出的系统对用户的日常行为进行建模，以检测异常行为，并有策略地生成干预措施，以鼓励有利于健康生活方式的行为。该系统使用基于Mamdani型模糊规则的组件来预测每个检测到的异常所需的干预级别，并使用顺序决策算法Contextual Armed Bandit来生成建议以最大程度地减少异常行为。我们详细描述了系统的体系结构，并提供了异常检测和相应的健康反馈的示例实现。