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      A Deep Learning Approach for Recognizing Activity of Daily Living (ADL) for Senior Care: Exploiting Interaction Dependency and Temporal Patterns

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      MIS Quarterly
      MIS Quarterly

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          Abstract

          Ensuring the health and safety of senior citizens who live alone is a growing societal concern. The Activity of Daily Living (ADL) approach is a common means to monitor disease progression and the ability of these individuals to care for themselves. However, the prevailing sensor-based ADL monitoring systems primarily rely on wearable motion sensors, capture insufficient information for accurate ADL recognition, and do not provide a comprehensive understanding of ADLs at different granularities. Current healthcare IS and mobile analytics research focuses on studying the system, device, and provided services, and is in need of an end-to-end solution to comprehensively recognize ADLs based on mobile sensor data. This study adopts the design science paradigm and employs advanced deep learning algorithms to develop a novel hierarchical, multiphase ADL recognition framework to model ADLs at different granularities. We propose a novel 2D interaction kernel for convolutional neural networks to leverage interactions between human and object motion sensors. We rigorously evaluate each proposed module and the entire framework against state-of-the-art benchmarks (e.g., support vector machines, DeepConvLSTM, hidden Markov models, and topic-modeling-based ADLR) on two real-life motion sensor datasets that consist of ADLs at varying granularities: Opportunity and INTER. Results and a case study demonstrate that our framework can recognize ADLs at different levels more accurately. We discuss how stakeholders can further benefit from our proposed framework. Beyond demonstrating practical utility, we discuss contributions to the IS knowledge base for future design science-based cybersecurity, healthcare, and mobile analytics applications.

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          Most cited references68

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

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              Assessing self-maintenance: activities of daily living, mobility, and instrumental activities of daily living.

              S. Katz (1983)
              The aging of the population of the United States and a concern for the well-being of older people have hastened the emergence of measures of functional health. Among these, measures of basic activities of daily living, mobility, and instrumental activities of daily living have been particularly useful and are now widely available. Many are defined in similar terms and are built into available comprehensive instruments. Although studies of reliability and validity continue to be needed, especially of predictive validity, there is documented evidence that these measures of self-maintaining function can be reliably used in clinical evaluations as well as in program evaluations and in planning. Current scientific evidence indicates that evaluation by these measures helps to identify problems that require treatment or care. Such evaluation also produces useful information about prognosis and is important in monitoring the health and illness of elderly people.
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                Author and article information

                Contributors
                Journal
                MIS Quarterly
                MISQ
                MIS Quarterly
                02767783
                21629730
                June 1 2021
                June 1 2021
                : 45
                : 2
                : 859-896
                Article
                10.25300/MISQ/2021/15574
                914301c6-79c4-479a-8a93-826f2106d48e
                © 2021
                History

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