Classification of various daily behaviors using deep learning and smart watch

Kwon, Min-Cheol; Ju, MinChul; Choi, Sunwoong

doi:10.1109/icufn.2017.7993888

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…For example, the device may encourage us to stand up if we sit still for a long time or request an SOS if we fall while alone. With the recent advances in sensors and wearable technologies, many studies have investigated using smartwatches as data-collection equipment [ 1 , 2 , 3 , 4 ]. To date, many HAR studies have focused on the coarse-grained classification of human movements, such as walking, running, sitting, and lying, each of which is a distinct activity.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Fine-Grained Walking Patterns Using a Smartwatch with Deep Attentive Neural Networks

Kim

Park

et al. 2021

Sensors

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Generally, people do various things while walking. For example, people frequently walk while looking at their smartphones. Sometimes we walk differently than usual; for example, when walking on ice or snow, we tend to waddle. Understanding walking patterns could provide users with contextual information tailored to the current situation. To formulate this as a machine-learning problem, we defined 18 different everyday walking styles. Noting that walking strategies significantly affect the spatiotemporal features of hand motions, e.g., the speed and intensity of the swinging arm, we propose a smartwatch-based wearable system that can recognize these predefined walking styles. We developed a wearable system, suitable for use with a commercial smartwatch, that can capture hand motions in the form of multivariate timeseries (MTS) signals. Then, we employed a set of machine learning algorithms, including feature-based and recent deep learning algorithms, to learn the MTS data in a supervised fashion. Experimental results demonstrated that, with recent deep learning algorithms, the proposed approach successfully recognized a variety of walking patterns, using the smartwatch measurements. We analyzed the results with recent attention-based recurrent neural networks to understand the relative contributions of the MTS signals in the classification process.

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Section: Introductionmentioning

confidence: 99%

Recognition of Fine-Grained Walking Patterns Using a Smartwatch with Deep Attentive Neural Networks

Kim

Park

et al. 2021

Sensors

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“…Several studies have focused on clustering and classification methods as well as on algorithms suitable for medical data. Examples include classification schemes to group human behaviors and daily activities into different categories [34], and mechanisms to detect patients' activities and events (e.g., sitting, falling) [35].…”

Section: Cluster 2: Datamentioning

confidence: 99%

Exploratory Analysis of Internet of Things (IoT) in Healthcare: A Topic Modeling Approach

Dantu¹,

Dissanayake²,

Nerur³

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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The rapid integration of the physical and cyber worlds through the Internet of Things, or IoTs, is transforming our lives in ways that we could not have imagined even five years ago. Although they are still in their infancy, IoTs have already made a significant impact, particularly in the healthcare domain. The purpose of this study is to unravel key themes latent in the sparse but growing academic literature on the application of IoTs in healthcare. Specifically, we performed topic modeling and identified five dominant clusters of research, namely, privacy and security, wireless network technologies, applications, data, and smart health and cloud. Our results show that research in healthcare IoT has mainly focused on the technical aspects with little attention to social concerns. In addition to categorizing and discussing the topics identified, the paper provides directions for future research.

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Exploratory Analysis of Internet of Things (IoT) in Healthcare: A Topic Modelling & Co-citation Approaches

Dantu

Dissanayake

Nerur

2020

Information Systems Management

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Classification of various daily behaviors using deep learning and smart watch

Cited by 7 publications

References 11 publications

Recognition of Fine-Grained Walking Patterns Using a Smartwatch with Deep Attentive Neural Networks

Recognition of Fine-Grained Walking Patterns Using a Smartwatch with Deep Attentive Neural Networks

Exploratory Analysis of Internet of Things (IoT) in Healthcare: A Topic Modeling Approach

Exploratory Analysis of Internet of Things (IoT) in Healthcare: A Topic Modelling & Co-citation Approaches

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