Model-based Digital Twins of Medicine Dispensers for Healthcare IoT
Applications
- URL: http://arxiv.org/abs/2312.04662v1
- Date: Thu, 7 Dec 2023 19:52:55 GMT
- Title: Model-based Digital Twins of Medicine Dispensers for Healthcare IoT
Applications
- Authors: Hassan Sartaj, Shaukat Ali, Tao Yue, Kjetil Moberg
- Abstract summary: We propose a model-based approach for the creation and operation of digital twins (DTs) of medicine dispensers.
We evaluate our approach with an industrial IoT system with medicine dispensers in the context of Oslo City.
- Score: 5.6001750995050985
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Healthcare applications with the Internet of Things (IoT) are often
safety-critical, thus, require extensive testing. Such applications are often
connected to smart medical devices from various vendors. System-level testing
of such applications requires test infrastructures physically integrating
medical devices, which is time and monetary-wise expensive. Moreover,
applications continuously evolve, e.g., introducing new devices and users and
updating software. Nevertheless, a test infrastructure enabling testing with a
few devices is insufficient for testing healthcare IoT systems, hence
compromising their dependability. In this paper, we propose a model-based
approach for the creation and operation of digital twins (DTs) of medicine
dispensers as a replacement for physical devices to support the automated
testing of IoT applications at scale. We evaluate our approach with an
industrial IoT system with medicine dispensers in the context of Oslo City and
its industrial partners, providing healthcare services to its residents. We
study the fidelity of DTs in terms of their functional similarities with their
physical counterparts: medicine dispensers. Results show that the DTs behave
more than 92% similar to the physical medicine dispensers, providing a faithful
replacement for the dispenser.
Related papers
- MeDeT: Medical Device Digital Twins Creation with Few-shot Meta-learning [3.229371159969159]
We propose a meta-learning-based approach to generate digital twins (DTs) of medical devices and adapt DTs to evolving devices.
We evaluate MeDeT in OsloCity's context using five widely-used medical devices integrated with a real-world healthcare IoT application.
arXiv Detail & Related papers (2024-10-04T16:43:53Z) - Uncertainty-Aware Environment Simulation of Medical Devices Digital Twins [3.229371159969159]
We propose a model-based approach (EnvDT) for modeling and simulating the environment of medical devices' digital twins under uncertainties.
We empirically evaluate the EnvDT using three medicine dispensers, Karie, Medido, and Pilly connected to a real-world IoT-based healthcare application.
arXiv Detail & Related papers (2024-10-04T15:17:52Z) - FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection [83.54960238236548]
FEDMEKI not only preserves data privacy but also enhances the capability of medical foundation models.
FEDMEKI allows medical foundation models to learn from a broader spectrum of medical knowledge without direct data exposure.
arXiv Detail & Related papers (2024-08-17T15:18:56Z) - Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey [53.691704671844406]
The Internet of things (IoT) can significantly enhance the quality of human life, specifically in healthcare.
The human digital twin (HDT) is proposed as an innovative paradigm that can comprehensively characterize the replication of the individual human body.
HDT is envisioned to empower IoT-healthcare beyond the application of healthcare monitoring by acting as a versatile and vivid human digital testbed.
Recently, generative artificial intelligence (GAI) may be a promising solution because it can leverage advanced AI algorithms to automatically create, manipulate, and modify valuable while diverse data.
arXiv Detail & Related papers (2024-01-22T03:17:41Z) - MultiIoT: Benchmarking Machine Learning for the Internet of Things [70.74131118309967]
The next generation of machine learning systems must be adept at perceiving and interacting with the physical world.
sensory data from motion, thermal, geolocation, depth, wireless signals, video, and audio are increasingly used to model the states of physical environments.
Existing efforts are often specialized to a single sensory modality or prediction task.
This paper proposes MultiIoT, the most expansive and unified IoT benchmark to date, encompassing over 1.15 million samples from 12 modalities and 8 real-world tasks.
arXiv Detail & Related papers (2023-11-10T18:13:08Z) - HITA: An Architecture for System-level Testing of Healthcare IoT Applications [5.126355491416586]
This paper presents a real-world test infrastructure software architecture (HITA) designed for healthcare IoT applications.
We evaluate HITA's digital twin (DT) generation component implemented using model-based and machine learning (ML) approaches.
arXiv Detail & Related papers (2023-09-08T09:14:50Z) - IoMT-Blockchain based Secured Remote Patient Monitoring Framework for
Neuro-Stimulation Device [0.0]
Real-time sensory data from patients may be delivered and analyzed through rapid development of wearable IoMT devices.
Data from the Internet of Things is gathered, analyzed, and stored in a single location.
Due to its decentralized nature, blockchain (BC) can alleviate these issues.
arXiv Detail & Related papers (2023-08-31T16:59:58Z) - HEAR4Health: A blueprint for making computer audition a staple of modern
healthcare [89.8799665638295]
Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems.
Computer audition can be seen to be lagging behind, at least in terms of commercial interest.
We categorise the advances needed in four key pillars: Hear, corresponding to the cornerstone technologies needed to analyse auditory signals in real-life conditions; Earlier, for the advances needed in computational and data efficiency; Attentively, for accounting to individual differences and handling the longitudinal nature of medical data.
arXiv Detail & Related papers (2023-01-25T09:25:08Z) - Applications of Machine Learning in Healthcare and Internet of Things
(IOT): A Comprehensive Review [2.1270496914042996]
We present an extensive review of the state-of-the-art machine learning applications particularly in healthcare.
We highlight some open-ended issues of IoT in healthcare that leaves further research studies and investigation for scientists.
arXiv Detail & Related papers (2022-02-06T21:56:39Z) - Distantly supervised end-to-end medical entity extraction from
electronic health records with human-level quality [77.34726150561087]
We propose a novel method of doing medical EE from electronic health records ( EHR) as a single-step multi-label classification task.
Our model is trained end-to-end in a distantly supervised manner using targets automatically extracted from medical knowledge base.
Our work demonstrates that medical entity extraction can be done end-to-end without human supervision and with human quality given the availability of a large enough amount of unlabeled EHR and a medical knowledge base.
arXiv Detail & Related papers (2022-01-25T17:04:46Z) - MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence
using Federated Evaluation [110.31526448744096]
We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data.
We are building MedPerf, an open framework for benchmarking machine learning in the medical domain.
arXiv Detail & Related papers (2021-09-29T18:09:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.