Using the Internet of Things to Support Emotional Health

Eriksson, Jeanette; Russo, Nancy L.; Marín, Javier

doi:10.4108/eai.23-3-2018.154372

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Eriksson et al [17] designed personal emotion-tracking applications to conduct emotional evaluations and support emotion-sensing IoT systems. Chen et al [18] proposed a prototype system of Smart Home 2.0 to achieve the affective interactions between householders and greens.…”

Section: A Affective Computing In the Iotmentioning

confidence: 99%

Multistep Deep System for Multimodal Emotion Detection With Invalid Data in the Internet of Things

Xie

Lv³

et al. 2020

IEEE Access

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The Internet of Things (IoT) technologies such as interconnection and edge computing help emotion recognition to be applied in healthcare, smart education, etc. However, the acquisition and transmission processes may have some situations, such as lost signals and serious interference noise caused by motion, which affect the quality of the received data and limit the performance of IoT emotion detection. We collectively refer to these as invalid data. A multi-step deep (MSD) system is proposed to reliably detect multimodal emotion by the collected records containing invalid data. Semantic compatibility and continuity are utilized to filter out the invalid data. The feature from invalid modal data is replaced through the imputation method to compensate for the impact of invalid data on emotion detection. In this way, the proposed system can automatically process invalid data and improve the recognition performance. Furthermore, considering the spatiotemporal information, the features of video and physiological signals are extracted by specific deep neural networks in the MSD system. The simulation experiments are conducted on a public multimodal database, and the performance of the MSD system measured by the unweighted average recall is better than that of the traditional system. The promising results observed in the experiments verify the potential influence of the proposed system in practical IoT applications. INDEX TERMS Internet of Things, multimodal emotion detection, invalid data, multi-step deep (MSD) system, deep neural networks

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Section: A Affective Computing In the Iotmentioning

confidence: 99%

Multistep Deep System for Multimodal Emotion Detection With Invalid Data in the Internet of Things

Xie

Lv³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

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Internet of Things (IoT) Enabled Architecture for Social Distancing During Pandemic

et al. 2021

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Social distancing has been regarded as a key method by the authorities worldwide to manage the pandemic of COVID-19. Digital technologies play a crucial role to support the social, professional and economic activities when people are forced to stay locked-down in their homes. Internet of things (IoT) technologies have a track of providing high quality remote health care and automation services which could guarantee social distancing while maintaining health and well-being of populations. In this paper, we propose an end-to-end IoT architecture to support the social distancing in the event of pandemic. The architecture comprises of the major use cases of IoT in relevance with the COVID-19. Furthermore, we also present a short-term and long-term strategy to mange the social distancing methodology using the proposed IoT architecture. The challenges associated with each layer of architecture have been highlighted and design guidelines have been presented to deal with them.

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Using the Internet of Things to Support Emotional Health

Cited by 2 publications

References 20 publications

Multistep Deep System for Multimodal Emotion Detection With Invalid Data in the Internet of Things

Multistep Deep System for Multimodal Emotion Detection With Invalid Data in the Internet of Things

Internet of Things (IoT) Enabled Architecture for Social Distancing During Pandemic

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