Human Body Digital Twin: A Master Plan
- URL: http://arxiv.org/abs/2307.09225v2
- Date: Tue, 12 Sep 2023 19:57:52 GMT
- Title: Human Body Digital Twin: A Master Plan
- Authors: Chenyu Tang, Wentian Yi, Edoardo Occhipinti, Yanning Dai, Shuo Gao,
and Luigi G. Occhipinti
- Abstract summary: A human body digital twin (DT) is a virtual representation of an individual's physiological state, created using real-time data from sensors and medical test devices.
The human body DT has the potential to revolutionize healthcare and wellness, but its responsible and effective implementation requires consideration of various factors.
This article presents a comprehensive overview of the current status and future prospects of the human body DT and proposes a five-level roadmap for its development.
- Score: 1.0812071024158496
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A human body digital twin (DT) is a virtual representation of an individual's
physiological state, created using real-time data from sensors and medical test
devices, with the purpose of simulating, predicting, and optimizing health
outcomes through advanced analytics and simulations. The human body DT has the
potential to revolutionize healthcare and wellness, but its responsible and
effective implementation requires consideration of various factors. This
article presents a comprehensive overview of the current status and future
prospects of the human body DT and proposes a five-level roadmap for its
development. The roadmap covers the development of various components, such as
wearable devices, data collection, data analysis, and decision-making systems.
The article also highlights the necessary support, security, cost, and ethical
considerations that must be addressed in order to ensure responsible and
effective implementation of the human body DT. The proposed roadmap provides a
framework for guiding future development and offers a unique perspective on the
future of the human body DT, facilitating new interdisciplinary research and
innovative solutions in this rapidly evolving field.
Related papers
- Zero Shot Health Trajectory Prediction Using Transformer [11.660997334071952]
Enhanced Transformer for Health Outcome Simulation (ETHOS) is a novel application of the transformer deep-learning architecture for analyzing health data.
ETHOS is trained using Patient Health Timelines (PHTs)-detailed, tokenized records of health events-to predict future health trajectories.
arXiv Detail & Related papers (2024-07-30T18:33:05Z) - Data Augmentation in Human-Centric Vision [54.97327269866757]
This survey presents a comprehensive analysis of data augmentation techniques in human-centric vision tasks.
It delves into a wide range of research areas including person ReID, human parsing, human pose estimation, and pedestrian detection.
Our work categorizes data augmentation methods into two main types: data generation and data perturbation.
arXiv Detail & Related papers (2024-03-13T16:05:18Z) - 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) - A Revolution of Personalized Healthcare: Enabling Human Digital Twin
with Mobile AIGC [54.74071593520785]
Mobile AIGC can be a key enabling technology for an emerging application, called human digital twin (HDT)
HDT empowered by the mobile AIGC is expected to revolutionize the personalized healthcare by generating rare disease data, modeling high-fidelity digital twin, building versatile testbeds, and providing 24/7 customized medical services.
arXiv Detail & Related papers (2023-07-22T15:59:03Z) - From the digital twins in healthcare to the Virtual Human Twin: a
moon-shot project for digital health research [3.380330348681461]
This position paper lays the conceptual foundations for developing the Virtual Human Twin.
The VHT infrastructure aims to facilitate academic researchers, public organisations, and the biomedical industry.
This paper is intended as a starting point for the consensus process and a call to arms for all stakeholders.
arXiv Detail & Related papers (2023-03-27T08:32:34Z) - Automatic Estimation of Anthropometric Human Body Measurements [0.0]
This paper formulates a research in the field of deep learning and neural networks, to tackle the challenge of body measurements estimation from various types of visual input data.
Also, we deal with the lack of real human data annotated with ground truth body measurements required for training and evaluation, by generating a synthetic dataset of various human body shapes.
arXiv Detail & Related papers (2021-12-22T16:13:59Z) - TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild [77.59069361196404]
TRiPOD is a novel method for predicting body dynamics based on graph attentional networks.
To incorporate a real-world challenge, we learn an indicator representing whether an estimated body joint is visible/invisible at each frame.
Our evaluation shows that TRiPOD outperforms all prior work and state-of-the-art specifically designed for each of the trajectory and pose forecasting tasks.
arXiv Detail & Related papers (2021-04-08T20:01:00Z) - Deep Learning-Based Human Pose Estimation: A Survey [66.01917727294163]
Human pose estimation has drawn increasing attention during the past decade.
It has been utilized in a wide range of applications including human-computer interaction, motion analysis, augmented reality, and virtual reality.
Recent deep learning-based solutions have achieved high performance in human pose estimation.
arXiv Detail & Related papers (2020-12-24T18:49:06Z) - BiteNet: Bidirectional Temporal Encoder Network to Predict Medical
Outcomes [53.163089893876645]
We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey.
An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys.
We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
arXiv Detail & Related papers (2020-09-24T00:42:36Z) - Graph representation forecasting of patient's medical conditions:
towards a digital twin [0.0]
We show the results of the investigation of pathological effects of overexpression of ACE2 across different signalling pathways in multiple tissues on cardiovascular functions.
We provide a proof of concept of integrating a large set of composable clinical models using molecular data.
arXiv Detail & Related papers (2020-09-17T13:49:48Z)
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.