Digital Twin of a Musculoskeletal System

George, Abraham; Bharat, C.R; Singh, Trisha; Sahil, Chandra Shekhar

doi:10.1109/icaia57370.2023.10169727

Cited by 1 publication

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This information can be used for the digital twin to predict the muscle activities and movement of the human body. This works in conjunction with the inertial sensing unit to accurately model and predict the subject’s position and movement [ 60 , 61 ]. Digital twins representing the musculoskeletal system can also be used to predict the future development of musculoskeletal system disorders [ 62 ].…”

Section: Healthcare Digital Twinmentioning

confidence: 99%

Digital Twins for Healthcare Using Wearables

Johnson,

Saikia

2024

Bioengineering

View full text Add to dashboard Cite

Digital twins are a relatively new form of digital modeling that has been gaining popularity in recent years. This is in large part due to their ability to update in real time to their physical counterparts and connect across multiple devices. As a result, much interest has been directed towards using digital twins in the healthcare industry. Recent advancements in smart wearable technologies have allowed for the utilization of human digital twins in healthcare. Human digital twins can be generated using biometric data from the patient gathered from wearables. These data can then be used to enhance patient care through a variety of means, such as simulated clinical trials, disease prediction, and monitoring treatment progression remotely. This revolutionary method of patient care is still in its infancy, and as such, there is limited research on using wearables to generate human digital twins for healthcare applications. This paper reviews the literature pertaining to human digital twins, including methods, applications, and challenges. The paper also presents a conceptual method for creating human body digital twins using wearable sensors.

show abstract

Section: Healthcare Digital Twinmentioning

confidence: 99%