Recognizing complex instrumental activities of daily living using scene information and fuzzy logic
Recognizing complex instrumental activities of daily living using scene information and fuzzy logic
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DOI：
10.1016/j.cviu.2015.04.005
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发表时间：
2015-11-01
影响因子：
4.5
通讯作者：
Skubic, Marjorie
中科院分区：
文献类型：
--
作者：
Banerjee, Tanvi;Keller, James M.;Skubic, Marjorie
我们描述了一种新的技术，结合联合收割机运动数据与场景信息捕捉活动的特点，老年人使用一个单一的微软Kinect深度传感器。具体而言，我们描述了一种方法来学习日常生活活动（ADL）和工具性ADL（IADL），以研究老年人的行为模式，以检测健康变化。为了学习ADL，我们结合场景信息来提供上下文信息来构建我们的活动模型。我们的算法的优势在于它的通用性，以不同的ADL模型，同时添加更多的信息，我们实例化ADL从学习的活动状态的模型。我们验证我们的结果在一个受控的环境中，并将其与另一个广泛接受的分类，隐马尔可夫模型（HMM）及其变化。我们还测试了我们的系统在一个动态的非结构化环境中收集的深度数据在TigerPlace，一个独立的老年人生活设施。一个家庭活动监测系统将受益于我们的算法，以提醒医疗保健提供者的ADL行为模式的显着时间变化的体弱老年人跌倒风险，认知障碍，和其他健康变化。(C)2015 Elsevier Inc. All rights reserved.
We describe a novel technique to combine motion data with scene information to capture activity characteristics of older adults using a single Microsoft Kinect depth sensor. Specifically, we describe a method to learn activities of daily living (ADLs) and instrumental ADLs (IADLs) in order to study the behavior patterns of older adults to detect health changes. To learn the ADLs, we incorporate scene information to provide contextual information to build our activity model. The strength of our algorithm lies in its generalizability to model different ADLs while adding more information to the model as we instantiate ADLs from learned activity states. We validate our results in a controlled environment and compare it with another widely accepted classifier, the hidden Markov model (HMM) and its variations. We also test our system on depth data collected in a dynamic unstructured environment at TigerPlace, an independent living facility for older adults. An in-home activity monitoring system would benefit from our algorithm to alert healthcare providers of significant temporal changes in ADL behavior patterns of frail older adults for fall risk, cognitive impairment, and other health changes. (C) 2015 Elsevier Inc. All rights reserved.