Visualizing Inertial Data For Wearable Sensor Based Daily Life Activity Recognition Using Convolutional Neural Network*

AbstractA human activity recognition (HAR) system acts as the backbone of many human-centric applications, such as active assisted living and in-home monitoring for elderly and physically impaired people. Although existing Wi-Fi-based human activity recognition methods report good results, their performance is affected by the changes in the ambient environment. In this work, we present Wi-Sense—a human activity recognition system that uses a convolutional neural network (CNN) to recognize human activities based on the environment-independent fingerprints extracted from the Wi-Fi channel state information (CSI). First, Wi-Sense captures the CSI by using a standard Wi-Fi network interface card. Wi-Sense applies the CSI ratio method to reduce the noise and the impact of the phase offset. In addition, it applies the principal component analysis to remove redundant information. This step not only reduces the data dimension but also removes the environmental impact. Thereafter, we compute the processed data spectrogram which reveals environment-independent time-variant micro-Doppler fingerprints of the performed activity. We use these spectrogram images to train a CNN. We evaluate our approach by using a human activity data set collected from nine volunteers in an indoor environment. Our results show that Wi-Sense can recognize these activities with an overall accuracy of 97.78%. To stress on the applicability of the proposed Wi-Sense system, we provide an overview of the standards involved in the health information systems and systematically describe how Wi-Sense HAR system can be integrated into the eHealth infrastructure.

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Identification of Research Trends in Aging in Place Using Text Mining Analysis

Innovation in Aging ◽

10.1093/geroni/igaa057.163 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 50-50

Author(s):

Ha Neul Kim ◽

Seok In Nam

Keyword(s):

Quality Of Life ◽

Older Adults ◽

Text Mining ◽

Independent Living ◽

Daily Life ◽

Aging In Place ◽

Research Trends ◽

Life Activity ◽

Daily Life Activity

Abstract Since 1980s professionals and social service providers have focused on aging at the place where people lived. This is the initial concept of the Aging in Place (AIP). Over 40 years, the topics have developed and extended to other disciplines welcoming different perspectives in the study of AIP. Therefore, this study aims to understand the overall research trends in Aging in Place (AIP) studies using text mining analysis to track the evolvement of AIP subtopics not only in Gerontology but also in various fields. To identify the topic trends, we collected the titles, abstracts, and keywords from 1,372 international articles that were published from 1981 to 2019. Then, keywords were extracted and cleaned based on precedent literature and discussions. We analyzed the keywords based on the degree of centrality and visualized the keyword-networks using VOSviewer and Pajek. Top-most popular keywords are “independent living”, “housing”, “older adults”, “home care”, “daily life activity” and “quality of life.” The change in topic trends shows that in the 1980s to early-2000s, research focused on organization and management level of intervention, home(housing) for the older adults, long term care. In the mid-2010s, health-related topics such as daily life activity, health service, health care delivery and quality of life have emerged. Recently, the topics have extended further to technology, caregiver, well-being, and environment design, environmental planning that support independent living of oneself. The research result shows that the interdisciplinary approach regarding AIP is not only inevitable but also encouraged for an in-depth discussion of the field.

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