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- Ji, Guangchao, et al.
(author)
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Skin-attached sensor and artifact removal using cloud computing
- 2015
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In: Proceedings of the International Conferences on e-Health 2015, EH 2015, e-Commerce and Digital Marketing 2015, EC 2015 and Information Systems Post-Implementation and Change Management 2015, ISPCM 2015 - Part of the Multi Conference on Computer Science and Information Systems 2015. - : IADIS. - 9789898533425 ; , s. 233-234
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Conference paper (peer-reviewed)abstract
- In this paper we present user-centric and user-friendly technical solutions for the proactive healthcare, in particular for senior citizens. The main objective is to develop a skin attached sensing device by leveraging miniaturized biomedical circuits and flexible sensor technology. Digital signal processing algorithms are applied on the Cloud server for near real-time signal shaping and noise removal.
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- Yang, Geng, et al.
(author)
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IoT-Based Remote Pain Monitoring System : From Device to Cloud Platform
- 2018
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In: IEEE journal of biomedical and health informatics. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2168-2194 .- 2168-2208. ; 22:6, s. 1711-1719
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Journal article (peer-reviewed)abstract
- Facial expressions are among behavioral signs of pain that can be employed as an entry point to develop an automatic human pain assessment tool. Such a tool can be an alternative to the self-report method and particularly serve patients who are unable to self-report like patients in the intensive care unit and minors. In this paper, a wearable device with a biosensing facial mask is proposed to monitor pain intensity of a patient by utilizing facial surface electromyogram (sEMG). The wearable device works as a wireless sensor node and is integrated into an Internet of Things (IoT) system for remote pain monitoring. In the sensor node, up to eight channels of sEMG can be each sampled at 1000 Hz, to cover its full frequency range, and transmitted to the cloud server via the gateway in real time. In addition, both low energy consumption and wearing comfort are considered throughout the wearable device design for long-term monitoring. To remotely illustrate real-time pain data to caregivers, a mobile web application is developed for real-time streaming of high-volume sEMG data, digital signal processing, interpreting, and visualization. The cloud platform in the system acts as a bridge between the sensor node and web browser, managing wireless communication between the server and the web application. In summary, this study proposes a scalable IoT system for real-time biopotential monitoring and a wearable solution for automatic pain assessment via facial expressions.
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