Enabling Ambient Intelligence of Things (AIoT) healthcare system architectures

Pise, Anil Audumbar; Yoon, Boram; Singh, Saurabh

doi:10.1016/j.comcom.2022.10.029

Cited by 17 publications

(3 citation statements)

References 33 publications

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“…On the other hand, [6] accentuated case studies through abstract experimentation without highlighting the technical information associated with data processing or model deployment. The impact of AIoT was evaluated in [7] for the healthcare domain. The authors performed a detailed exploration of various facets of IoT advancements specifically tailored to the medical field; however, their approach lacked specificity in addressing specific diseases, and did not emphasize how AMI could handle emergency cases.…”

Section: Recent Surveys On Ami and Diseasesmentioning

confidence: 99%

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

Sarkar,

Lee,

Sahoo

2024

Electronics

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Ambient Intelligence (AMI) represents a significant advancement in information technology that is perceptive, adaptable, and finely attuned to human needs. It holds immense promise across diverse domains, with particular relevance to healthcare. The integration of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT) to create an AMI environment in medical contexts further enriches this concept within healthcare. This survey provides invaluable insights for both researchers and practitioners in the healthcare sector by reviewing the incorporation of AMI techniques in the IoMT. This analysis encompasses essential infrastructure, including smart environments and spectrum for both wearable and non-wearable medical devices to realize the AMI vision in healthcare settings. Furthermore, this survey provides a comprehensive overview of cutting-edge AI methodologies employed in crafting IoMT systems tailored for healthcare applications and sheds light on existing research issues, with the aim of guiding and inspiring further advancements in this dynamic field.

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Section: Recent Surveys On Ami and Diseasesmentioning

confidence: 99%

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

Sarkar,

Lee,

Sahoo

2024

Electronics

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“…Based on the great needs that currently exist in the health sector, different health care experts are developing different smart devices and other medical technologies, as well as ambient intelligence, a key factor in the use of technologies such as the Internet of Things (IoT) [ 13 ]. Remote health surveillance can be used to track non-critically ill patients as opposed to referring them to a hospital, relieving pressure on hospital services, optimizing resources, and lowering the burden on healthcare professionals.…”

Section: Previous Work and Literature Reviewmentioning

confidence: 99%

Monitoring System for Operating Variables in Incubators in the Neonatology Service of a Highly Complex Hospital through the Internet of Things (IoT)

Aya-Parra,

Rodriguez-Orjuela,

Rodriguez Torres

et al. 2023

Sensors

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Background: Around 15 million premature babies are born annually, requiring specialized care. Incubators are vital for maintaining their body temperature, which is crucial for their well-being. Ensuring optimal conditions in incubators, including constant temperature, oxygen control, and comfort, is essential for improving the care and survival rates of these infants. Methods: To address this, an IoT-based monitoring system was developed in a hospital setting. The system comprised hardware components such as sensors and a microcontroller, along with software components including a database and a web application. The microcontroller collected data from the sensors, which was then transmitted to a broker via WiFi using the MQTT protocol. The broker validated and stored the data in the database, while the web application provided real-time access, alerts, and event recording. Results: Two certified devices were created, employing high quality components. The system was successfully implemented and tested in both the biomedical engineering laboratory and the neonatology service of the hospital. The results of the pilot test supported the concept of IoT-based technology, demonstrating satisfactory responses in temperature, humidity, and sound variables within the incubators. Conclusions: The monitoring system facilitated efficient record traceability, allowing access to data over various timeframes. It also captured event records (alerts) related to variable problems, providing information on duration, date, hour, and minutes. Overall, the system offered valuable insights and enhanced monitoring capabilities for neonatal care.

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“…In response to these challenges, emerging technologies such as Federated Learning (FL) and the Internet of Things (IoT) have garnered attention for their potential to revolutionize healthcare practices. These technologies have the potential to revolutionize healthcare in multiple ways [6]. The decentralized machine learning approach known as Federated Learning makes it possible to train models in a collaborative manner across multiple devices that are located in different locations without the need for centralized data aggregation.…”

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confidence: 99%

Towards a Holistic Approach to Chronic Disease Management: Integrating Federated Learning and IoT for Personalized health Care

Dipika R. Birari

2024

jes

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Chronic diseases, specifically Cardiovascular Disease (CVD), pose a substantial worldwide health obstacle, requiring innovative and comprehensive approaches to management. This study presents an innovative method for managing chronic diseases by combining Federated Learning (FL) and the Internet of Things (IoT). The goal is to offer tailored healthcare solutions. The study presents a new approach called Federated Transfer Learning (FTL) that incorporates Adaptive Gradient Clipping (AGC) to improve the performance of models and maintain privacy in a distributed network of healthcare devices. The primary objective of this study is to develop a holistic framework that seamlessly amalgamates data from various sources, including wearables, medical devices, and electronic health records, utilizing FL to build a centralized model while preserving data privacy at the individual level. The proposed Faster Than Light (FTL) algorithm with Automatic Gain Control (AGC) enhances the process of model convergence, guaranteeing stability and dependability in a federated learning environment. The research methodology entails implementing the suggested framework in the management of Cardiovascular Disease (CVD). The performance of the model is assessed using the Area Under the Curve (AUC) metric, resulting in an impressive AUC score of 92.4%. This highlights the efficacy of the integrated approach in precisely forecasting and controlling risks associated with cardiovascular diseases (CVD), showcasing its potential for widespread use in managing chronic diseases. The key findings emphasize the advantages of integrating IoT devices into the FL ecosystem, enabling the immediate surveillance and gathering of various health data. The decentralized nature of the learning process enables model training to occur without compromising data privacy, making it suitable for large-scale healthcare systems. Moreover, the implementation of Faster Than Light (FTL) with Automatic Gain Control (AGC) improves the resilience and effectiveness of the federated learning procedure, thereby enhancing the overall achievement of the suggested framework. In conclusion, this study presents a groundbreaking contribution to the field of chronic disease management, specifically targeting Cardiovascular Disease. The combination of Federated Learning and IoT, along with the innovative Federated Transfer Learning with Adaptive Gradient Clipping, not only achieves outstanding predictive accuracy but also guarantees the privacy and security of sensitive health data. The proposed framework holds significant promise for revolutionizing personalized healthcare solutions, paving the way for a more effective and patient-centric approach to chronic disease management.

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Enabling Ambient Intelligence of Things (AIoT) healthcare system architectures

Cited by 17 publications

References 33 publications

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

Monitoring System for Operating Variables in Incubators in the Neonatology Service of a Highly Complex Hospital through the Internet of Things (IoT)

Towards a Holistic Approach to Chronic Disease Management: Integrating Federated Learning and IoT for Personalized health Care

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