Development of a wearable tele-monitoring system with IoT for bio-medical applications

Silva, A. H. T. E. De; Sampath, W. H. P.; Sameera, N. H. L.; Amarasinghe, Y. W. R.; Mitani, Atsushi

doi:10.1109/gcce.2016.7800404

Cited by 10 publications

(4 citation statements)

References 4 publications

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“…The "Smart wearable system for safety-related medical IoT application: Case of epileptic patient working in an industrial environment" was carried out by Hayek, et al [5]. The remote care-taking applications Silva, et al [8] "designed could be implemented for patients. This system is used as a waist belt and shoe with embedded sensors.…”

Section: Related Workmentioning

confidence: 99%

A Model for Pervasive Computing and Wearable Devices for Sustainable Healthcare Applications

Dewi,

Thinakan,

Batumalay

et al. 2023

IJACSA

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The user's demands in the system supported by the Internet of Things are frequently controlled effectively using the pervasive computing system. Pervasive computing is a term used to describe a system that integrates several communication and distributed network technologies. Even so, it properly accommodates user needs. It is quite difficult to be inventive in the pervasive computing system when it comes to the delivery of information, handling standards, and extending heterogeneous aid for scattered clients. In this view, our paper intends to utilize a Dispersed and Elastic Computing Model (DECM) to enable proper and reliable communication for people who are using IoT-based wearable healthcare devices. Recurrent Reinforcement Learning (RRL) is used in the suggested model and the system that is connected to analyze resource allocation in response to requirements and other allocative factors. To provide effective data transmission over wearable medical devices, the built system gives managing mobility additional consideration to resource allocation and distribution. The results show that the pervasive computing system provides services to the user with reduced latency and an increased rate of communication for healthcare wearable devices based on the determined demands of the resources. This is an important aspect of sustainable healthcare. We employ the assessment metrics consisting of request failure, response time, managed and backlogged requests, bandwidth, and storage to capture the consistency of the proposed model.

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Section: Related Workmentioning

confidence: 99%

A Model for Pervasive Computing and Wearable Devices for Sustainable Healthcare Applications

Dewi,

Thinakan,

Batumalay

et al. 2023

IJACSA

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“…Regarding the specific sensors that are used, as well as the architecture of the sensor network and the flow of collected data, the works of A. Kor et al [12] and A.H.T.E. De Silva et al [7] make obvious the relevance and applicability of the Internet of Things and Quantified-Self concepts in healthcare and mHealth. Finally, B. D. Weinberg et al [27] have examined the privacy concerns that accompany the transition from Web 2.0 to Internet of Things.…”

Section: Related Workmentioning

confidence: 99%

Advancing Quantified-Self Applications Utilizing Visual Data Analytics and the Internet of Things

Chousiadas

Menychtas

Tsanakas

2018

IFIP Advances in Information and Communication Technology

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The exponential growth of the number and variety of IoT devices and applications for personal use, as well as the improvement of their quality and performance, facilitates the realization of intelligent eHealth concepts. Nowadays, it is easier than ever for individuals to monitor themselves, quantify and log their everyday activities in order to gain insights about their body performance and receive recommendations and incentives to improve it. Of course, in order for such systems to live up to the promise, given the treasure trove of data that is collected, machine learning techniques need to be integrated in the processing and analysis of the data. This systematic and automated quantification, logging and analysis of personal data, using IoT and AI technologies, has given birth to the phenomenon of Quantified-Self. This work proposes a prototype decentralized Quantified-Self application, built on top of a dedicated IoT gateway, that aggregates and analyses data from multiple sources, such as biosignal sensors and wearables, and performs analytics and visualization on it.

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“…Fall detection seeks to automate the alerts and notification system and provide assistance whenever an accident such as a fall occurs [8]. Some detectors provide wearable devices to monitor the user's biomedical data in realtime [9]. This is a notable area because both the number and quality of sensors in smartphones and tracking devices are continually growing.…”

Section: Introductionmentioning

confidence: 99%

FallDeF5: A Fall Detection Framework Using 5G-Based Deep Gated Recurrent Unit Networks

et al. 2021

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Fall prevalence is high among elderly people, which is challenging due to the severe consequences of falling. This is why rapid assistance is a critical task. Ambient assisted living (AAL) uses recent technologies such as 5G networks and the internet of medical things (IoMT) to address this research area. Edge computing can reduce the cost of cloud communication, including high latency and bandwidth use, by moving conventional healthcare services and applications closer to end-users. Artificial intelligence (AI) techniques such as deep learning (DL) have been used recently for automatic fall detection, as well as supporting healthcare services. However, DL requires a vast amount of data and substantial processing power to improve its performance for the IoMT linked to the traditional edge computing environment. This research proposes an effective fall detection framework based on DL algorithms and mobile edge computing (MEC) within 5G wireless networks, the aim being to empower IoMT-based healthcare applications. We also propose the use of a deep gated recurrent unit (DGRU) neural network to improve the accuracy of existing DL-based fall detection methods. DGRU has the advantage of dealing with time-series IoMT data, and it can reduce the number of parameters and avoid the vanishing gradient problem. The experimental results on two public datasets show that the DGRU model of the proposed framework achieves higher accuracy rates compared to the current related works on the same datasets.

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Development of a wearable tele-monitoring system with IoT for bio-medical applications

Cited by 10 publications

References 4 publications

A Model for Pervasive Computing and Wearable Devices for Sustainable Healthcare Applications

A Model for Pervasive Computing and Wearable Devices for Sustainable Healthcare Applications

Advancing Quantified-Self Applications Utilizing Visual Data Analytics and the Internet of Things

FallDeF5: A Fall Detection Framework Using 5G-Based Deep Gated Recurrent Unit Networks

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