Healthy Aging: A Deep Meta-Class Sequence Model to Integrate Intelligence in Digital Twin
Healthy Aging: A Deep Meta-Class Sequence Model to Integrate Intelligence in Digital Twin
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DOI：
10.1109/jtehm.2023.3274357
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发表时间：
2023
影响因子：
3.4
通讯作者：
Muhammad Fahim;Vishal Sharma;R. Hunter;T. Duong
中科院分区：
文献类型：
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作者：
Muhammad Fahim;Vishal Sharma;R. Hunter;T. Duong
目的：老年人在家中的行为监测和对医疗从业者的日常生活活动分析是一个关键的挑战。方法和步骤：我们的框架将养老院复制到数字空间中，为监测居民的日常生活活动提供了一种不引人注目的方式。通过引入深度元类序列模型，解决了家庭中不同表演活动带来的学习挑战。其概念是根据活动的性质将活动集分组到单个元类中。它帮助基于长短期记忆(LSTM)的学习过程学习特征空间提取。每个元类抽象进一步分解为老年人在家中执行的单个活动。结果：实验是在自适应系统高级研究中心的数据集上进行的，所提出的模型与基线模型相比表现更好。临床影响：我们的发现展示了一个强大的框架来数字监控老年人的行为，这有助于医疗从业者了解老年人在家中执行日常任务或发生紧急情况的潜在风险所需的支持水平。
Objective: The behavior monitoring of older adults in their own home and enabling daily-life activity analysis to healthcare practitioner is a key challenge. Methods and procedures: Our framework replicates the elderly home in digital space which can provide an unobtrusive way to monitor the residents daily life activities. The learning challenges posed by different performed activities at home are solved by introducing the deep meta-class sequence model. The notion is to group the set of activities into a single meta-class according to the nature of the activities. It helps the learning process, which is based on long short-term memory (LSTM) to learn feature space abstraction. Each meta-class abstraction is further decomposed to an individual activity performed by the elderly at home. Results: The experiments are carried out over the Center for Advanced Studies in Adaptive Systems dataset and proposed model outperforms as compared to baseline models. Clinical impact: Our findings demonstrate a robust framework to digitally monitor the elderly behavior, which is beneficial for healthcare practitioners to understand the level of support the elderly needed to perform the daily tasks or potential risk of an emergency in their own homes.