HospiT'Win: A Predictive Simulation-Based Digital Twin for Patients Pathways in Hospital

Karakra, Abdallah; Fontanili, Franck; Lamine, Elyes; Lamothe, Jacques

doi:10.1109/bhi.2019.8834534

Cited by 52 publications

(27 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Liu et al (2019) proposed a digital twin healthcare system that supervises, diagnoses, and predicts the well-being of elderly people. Similarly, Karakra et al (2019) presented a DT framework of a hospital where healthcare providers can administer virtual health supervision, diagnostics, and future predictions based on patients' data.…”

Section: Dt In Other Industry Sectorsmentioning

confidence: 99%

Towards next generation cyber-physical systems and digital twins for construction

Akanmu¹,

Anumba²,

Ogunseiju³

2021

ITcon

View full text Add to dashboard Cite

The construction industry continues to seek innovative ways to safely, timely and cost-effectively deliver construction projects. Several efforts have been made to automate construction processes but marginial success has been achieved in effectively reducing the long standing risks suffered by the industry. While industry 4.0 promises to improve project efficiency, reduce waste and improve productivity, the transition to this will depend on the successful adoption of many emerging technologies such as virtual design modeling technologies, sensing technologies, data analysis, storage and communication technologies, human-computer interaction technologies, and robotics. To accelerate innovation, digital twins and cyber-physical systems will be a necessity to advance automation and real-time control with these technologies. While digital twin represents a digital replica of the asplanned and as-built facility, cyber physical systems involve integration of physical systems with their digital replica through sensors and actuators. Despite evidence of the efficacy of cyber-physical systems and digital twins for reducing non-fatal injuries, enhancing safety management, improving progress monitoring and enhancing performance monitoring and control of facilities, their adoption in the construction industry is still in its infancy. This paper sheds light on the opportunities offered by cyber-physical systems and digital twins in other industry sectors and advocates for their increased deployment in the construction industry. This paper describes cyber-physical integration of emerging technologies with the physical construction or constructed facility as the next generation digital twin and cyber-physical systems. Potential scenarios of next generation cyber physical system and digital twin for improving workforce productivity, health, and safety, lifecycle management of building systems, and workforce competency are presented.

show abstract

Section: Dt In Other Industry Sectorsmentioning

confidence: 99%

Towards next generation cyber-physical systems and digital twins for construction

Akanmu¹,

Anumba²,

Ogunseiju³

2021

ITcon

View full text Add to dashboard Cite

show abstract

“…Learning meaningful medical ontology representations within the EHR database can alleviate the data insufficiency problem, and the learned embeddings can cluster nicely into particular groups of diseases [ 151 ]. With simulation and motion data synthesis, digital twin models can be established for patient performance estimation and rehabilitation program planning to individual needs [ 152 , 153 ]. The twin model can serve as a counterpart of the patients to improve the wearability of sensor systems and transfer detailed human movement characteristics to real-world applications to reduce errors.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

Yerebakan

Dai

et al. 2022

Healthcare

View full text Add to dashboard Cite

With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnoses carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of the Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement unit (IMU)-based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data, especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML)-based techniques have been proposed to smartly map IMU-captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. A systematic search was conducted in four databases covering the aforementioned topic for articles published in the past six years. Eighty-one articles were included and discussed concerning two aspects: different ML techniques and application scenarios. This review yielded the conclusion that, with the help of ML technology, IMUs can serve as a crucial element in disease diagnosis, severity assessment, characteristic estimation, and monitoring during the rehabilitation process. Furthermore, it summarizes the state-of-the-art, analyzes challenges, and provides foreseeable future trends for developing IMU-ML systems for IoHT.

show abstract

“…In healthcare, they have been firstly implemented for allowing predictive maintenance of medical devices and for optimizing their performance in terms of examination speed and energy consumption, while other applications have been successfully developed to optimize hospital lifecycle [23]- [25].…”

Section: Research Background a Literature Review 1) Digital Twinmentioning

confidence: 99%

Human Digital Twin for Fitness Management

et al. 2020

View full text Add to dashboard Cite

Our research work describes a team of human Digital Twins (DTs), each tracking fitness-related measurements describing an athlete's behavior in consecutive days (e.g. food income, activity, sleep). After collecting enough measurements, the DT firstly predicts the physical twin performance during training and, in case of non-optimal result, it suggests modifications in the athlete's behavior. The athlete's team is integrated into SmartFit, a software framework for supporting trainers and coaches in monitoring and manage athletes' fitness activity and results. Through IoT sensors embedded in wearable devices and applications for manual logging (e.g. mood, food income), SmartFit continuously captures measurements, initially treated as the dynamic data describing the current physical twins' status. Dynamic data allows adapting each DT's status and triggering the DT's predictions and suggestions. The analyzed measurements are stored as the historical data, further processed by the DT to update (increase) its knowledge and ability to provide reliable predictions. Results show that, thanks to the team of DTs, SmartFit computes trustable predictions of the physical twins' conditions and produces understandable suggestions which can be used by trainers to trigger optimization actions in the athletes' behavior. Though applied in the sport context, SmartFit can be easily adapted to other monitoring tasks. INDEX TERMS Counterfactual explanations, digital twins, Internet of Things, machine learning, smart health, sociotechnical design, wearables.

show abstract

HospiT'Win: A Predictive Simulation-Based Digital Twin for Patients Pathways in Hospital

Cited by 52 publications

References 15 publications

Towards next generation cyber-physical systems and digital twins for construction

Towards next generation cyber-physical systems and digital twins for construction

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

Human Digital Twin for Fitness Management

Contact Info

Product

Resources

About