METHODS AND SYSTEMS FOR PROVISIONING DIGITAL DATA TO AN INDIVIDUAL

Disclosed herein is a system for provisioning digital data of an individual, in accordance with some embodiments. Accordingly, the system may include a communication device configured for receiving a request from a business provider device associated with a business provider. Further, the communication device may be configured for transmitting a data block of a digital twin data to the business provider device. Further, the system may include a processing device communicatively coupled to the communication device. Further, the processing device may be configured for processing the request. Further, the processing device may be configured for extracting the data block based on the processing. Further, the system may include a storage device communicatively coupled to the processing device. Further, the storage device may be configured for retrieving the data block.

FIELD OF THE INVENTION

Generally, the present disclosure relates to the field of data processing. More specifically, the present disclosure relates to methods and systems for provisioning digital data of an individual.

BACKGROUND OF THE INVENTION

Existing techniques for provisioning digital data of an individual are deficient with regard to several aspects. For instance, current technologies do not describe and represent multiple human characteristics (such as performance, physical attributes, behavior) associated with the individual in a unified form. Furthermore, current technologies lack bio-fidelity and scientific truth in describing human characteristics.

Therefore, there is a need for improved methods and systems for provisioning digital data to the individual that may overcome one or more of the above-mentioned problems and/or limitations.

SUMMARY OF THE INVENTION

Disclosed herein is a system for provisioning digital data of an individual, in accordance with some embodiments. Accordingly, the system may include a communication device configured for receiving a request from a business provider device associated with a business provider. Further, the request may include an identifier associated with the individual and at least one key associated with at least one data block of a digital twin data associated with the individual. Further, the digital twin data may include structural data representing at least one structure of the individual, at least one functional data representing at least one function of the individual, and behavioral data representing at least one behavior of the individual. Further, the communication device may be configured for transmitting the at least one data block of the digital twin data to the business provider device. Further, the business provider device may be configured to use the at least one data block to personalize at least one of a product and a service provisioned to the individual. Further, the system may include a processing device communicatively coupled to the communication device. Further, the processing device may be configured for processing the request. Further, the processing device may be configured for extracting the at least one data block based on the processing. Further, the system may include a storage device communicatively coupled to the processing device. Further, the storage device may be configured for retrieving the at least one data block.

Further disclosed herein is a method for provisioning digital data of an individual, in accordance with some embodiments. Accordingly, the method may include receiving, using a communication device, a request from a business provider device associated with a business provider. Further, the request may include an identifier associated with the individual and at least one key associated with at least one data block of a digital twin data associated with the individual. Further, the digital twin data may include structural data representing at least one structure of the individual, at least one functional data representing at least one function of the individual, and behavioral data representing at least one behavior of the individual. Further, the method may include processing, using a processing device, the request. Further, the method may include retrieving, using a storage device, the at least one data block. Further, the method may include extracting, using the processing device, the at least one data block based on the processing. Further, the method may include transmitting, using the communication device, the at least one data block of the digital twin data to the business provider device. Further, the business provider device may be configured to use the at least one data block to personalize at least one of a product and a service provisioned to the individual.

DETAIL DESCRIPTIONS OF THE INVENTION

The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in the context of methods and systems for provisioning digital data to an individual, embodiments of the present disclosure are not limited to use only in this context.

The present disclosure describes methods and systems for provisioning digital data of an individual. Further, every human may be different as each is unique and different from others. Further, the disclosed system creates individualized models for each person so that personal uniqueness is well described. Creating a digital human model for each person requires extensive work. To keep the effort tractable and affordable, the disclosed system uses standard models as the templates and then create individualized (instance) models from the template models via fitting, morphing, and scaling based on the personal data. Further, a Human Digital Twin (HDT) generated with the disclosed system provides different levels of details and fidelity of an individual human body.

Further, the HDT may be based on personal data and physics/first principles. Further, the HDT may be utilized in various human-centered applications, such as personalized products, services, training, education, and performance enhancement.

Further, the HDT may be used in spine surgery. The HDT of a patient can be used to simulate the effects of surgical options before the operation. Surgeons may use the results of the simulation to determine whether a specific surgery is right for the patient and what effects will have on the forces developed in the rest of the spine. Surgical device companies can also use the HDT with 3D printing to make patient-specific implants.

Further, the HDT may be used in exoskeletons. Further, the exoskeleton realizes its performance through intimate contact and interaction with a human user. The overall performance depends on many factors including the degree of coupling to the human. Further, the HDT with its human shape model and musculoskeletal model can be used to investigate numerous “what-if” design scenarios at a relatively low cost. This includes studying the effects of variability, determining the “best” geometries for performance, and to examine the linkage between form and function using virtual experimentation, to name a few.

Further, the HDT may be used for rehabilitation. Further, stroke is one of the leading causes of disability around the world. Whenever a stroke happens, the stroke survivor's brain commands cannot reach some muscles although those muscles could contract. Therefore, their body cannot perform the expected motion, thus hampering their activities of daily living. The objective of rehabilitation is to externally drive the human body to move to improve muscle movements through robotic devices. This human-robot interaction is a critical factor in the design of a successful robotic rehabilitation device. The HDT of a patient can be used to customize the design of the device or to adjust the setting of the device so that the effective human-robot interaction can be achieved to improve rehabilitation.

Further, the HDT may be used in a prosthesis. Further, the prosthesis refers to an artificial substitute or replacement of a part of the body. Prostheses for joints are the hip, knee, elbow, ankle, and finger joints. Prosthetic designs need to be personalized to accommodate the different needs of different customers. From the HDT, the human shape model can be used in 3D printing to create the replacement which just fits, a musculoskeletal model can be used to simulate and predict biomechanical effects of replacement on normal movement, and a full-body FE model can predict the stress in the joint being replaced.

Further, the HDT may be used in a personalized safety system in vehicles. Human injury risks are highly affected by the occupant size and shape, and material properties of human biological tissues. Therefore, the full-body finite element model from the HDT of a specific occupant can greatly enhance the understanding of how to design a safety system to protect the occupant better. It can be imagined that future vehicle manufactures can provide a specific set of safety configuration parameters based on the digital twin of a customer. When the customer is using the vehicle, the safety system can rapidly pre-set the configuration parameters to optimize the protection of the occupants in motor vehicle crashes. This personalized safety design concept can also be applied to other safety systems interacting with a human, such as helmets, sports shoes, etc.

Further, the HDT may be useful to remote medicine and tele healthcare. Further, the HDT, with a higher level of details and bio-fidelity derived from personal MRI, CT, or other forms of medical images, can be used in remote surgery or the augmented reality assisted surgery (ARAS).

Further, the HDTs may become valuable assets for both individuals and businesses. For an individual, having his digital twin can substantially improve the quality of life with personalized products and personalized services that best fit his needs. For a business, by utilizing the data provided by the HDT, the business will be able to create products and services to best fit an individual customer, adding values to the products and services thus differentiating from others.

Further, the disclosed system may be configured for generating invariants of HDT, that may include a Warfighter Digital Twin (WDT), but are not limited to, Firefighter Digital Twin (FDT), an Astronaut Digital Twin (ADT), and any other type of HDT that is specifically constructed for a specific group of people who share common features/characteristic, conditions, and requirements. Further, the WDT may include a special HDT specifically constructed for warfighters. Further, the FDT may include a special HDT specifically constructed for firefighters. Further, the ADT may include a special HDT specifically constructed for astronauts.

Further, the Human Digital Twin (HDT) may include a digital replica of an individual human. Further, the HDT digitally represents a multitude of features, attributes, characteristics, performance, and behavior of an individual human based on the personal data and physics/first principles. The HDT may include the individualized and unified digital models that may provide a compact, parameterized representation of an individual human and the data containers of personal physical, biological, and physiological data. The HDT may be used in human-centered applications including personalized products and services. Further, the HDT may include individualized and unified digital models that provide compact, parameterized representation of an individual human and data containers which collect and manage personal physical, biological, and physiological data. Further, the HDT may incorporate and utilize the data from, for example, wearable sensors to provide real-time, dynamic monitoring, analysis, and synchronized representation of its twin, the real biological human. The HDT can be used in human-centered applications including personalized products and services, by furnishing specific data of an individual to a potential user of the data upon the request from the user and the authorization by the owner of the HDT.

Further, the disclosed system may include digital human models which describe particular aspects of human features or characteristics for a group of people, avatars which use computer graphics to virtually mimic certain human features (e.g., human shape, facial expression, and human motion), and human agents which incorporate human data into particular forms to represent particular attributes or behaviors.

Further, the disclosed method may facilitate the generation of the HDT. Further, the disclosed method may include steps of (1) template model creation; (2) model unification; (3) individualization; and (d) data collection and management.

1) Template model creation: To reduce the efforts required for generating the HDT and expedite the generating process, the state-of-the-art standard models may be built in advance and then used as the template models for the individualized models.

2) Model unification: Conventionally, human body shape (with anthropometry), muscular-skeleton, and full-body anatomical structure are described separately by three independent models of a different type. However, human features/characteristics are inter-dependent and interactive. In particular, human body shape, muscular-skeleton, and full-body anatomical structure all are about human physical features and characteristics. Therefore, these models, each of which describes specific aspects of human, are integrated into a framework with a unified model structure and data structure to share common data and to facilitate information exchange. The unified model structure and data structure remain the same for every individual. The framework utilizes common features (data) shared among different models and implement unification by using the methods of fitting, scaling, and morphing.

3) Individualization: Humans are the same as we share common structures and characteristics. Therefore, the disclosed system creates a unified model structure and data structure, and standard models (templates) that will be used for all humans. Humans are different as each is unique and different from others. Therefore, the disclosed system creates individualized models for each person so that personal uniqueness is well described. Creating a digital human model for each person requires extensive work. To keep the effort tractable and affordable, this invention uses standard models as the templates and then create individualized (instance) models from the template models via fitting, morphing, and scaling based on the personal data. Further, the personal data used associated with the disclosed system include but are not limited to 2D imagery, 3D body scan, motion capture data, magnetic resonance imaging (MRI) data, and computed tomography (CT) data.

4) Data collection and management: Some human features or status are not suitable to be represented by a model or need to be updated frequently or monitored in real-time. Therefore, in reference toFIG. 5, an HDT data management system is created to collect data from various sources which include but are not limited to personal medical records, personal medical test reports, and wearable sensors put on an individual. Further, the HDT data management system may transfer the data into respective data containers. Further, the HDT data management system may store and manage the data with a data server. The personal data associated with HDT can be categorized in terms of physical, biological, and physiological features/characteristics. The personal data associated with HDT can be categorized in terms of static or dynamic. The personal data associated with HDT can be categorized in terms of discrete or continuous (sequential). The personal data associated with HDT can be categorized in terms of 1-D, 2-D, or 3-D; and (e) images, videos, signals, digital records, and text records.

Referring now to figures,FIG. 1is an illustration of an online platform100consistent with various embodiments of the present disclosure. By way of non-limiting example, the online platform100for provisioning digital data of an individual may be hosted on a centralized server102, such as, for example, a cloud computing service. The centralized server102may communicate with other network entities, such as, for example, a mobile device106(such as a smartphone, a laptop, a tablet computer, etc.), other electronic devices110(such as desktop computers, server computers, etc.), databases114, and sensors116over a communication network104, such as, but not limited to, the Internet. Further, users of the online platform100may include relevant parties such as, but not limited to, end-users, administrators, service providers, service consumers, and so on. Accordingly, in some instances, electronic devices operated by the one or more relevant parties may be in communication with the platform.

A user112, such as the one or more relevant parties, may access online platform100through a web-based software application or browser. The web-based software application may be embodied as, for example, but not be limited to, a website, a web application, a desktop application, and a mobile application compatible with a computing device1400.

FIG. 2is a block diagram of a system200for provisioning digital data of an individual, in accordance with some embodiments. The provisioning of digital data may include generating, updating and providing access to a digital twin under the same umbrella term, thus preserving unity of disclosure.

Further, the system200may include a communication device202configured for receiving a request from a business provider device associated with a business provider. Further, the request may include an identifier associated with the individual and at least one key associated with at least one data block of a digital twin data associated with the individual. Further, in some embodiments, the individual may include a human being. Further, the digital twin data may include structural data representing at least one structure of the individual, at least one functional data representing at least one function of the individual, and behavioral data representing at least one behavior of the individual. Further, the communication device202may be configured for transmitting the at least one data block of the digital twin data to the business provider device. Further, the business provider device may be configured to use the at least one data block to personalize at least one of a product and a service provisioned to the individual. Further, the system200may include a processing device204communicatively coupled to the communication device202. Further, the processing device204may be configured for processing the request. Further, the processing device204may be configured for extracting the at least one data block based on the processing. This may include selectively extracting portions of the digital twin data and providing it to requesting parties. Further, the system200may include a storage device206communicatively coupled to the processing device204. Further, the storage device206may be configured for retrieving the at least one data block.

Further, in some embodiments, the individual may include a non-human being, such as, for example, an animal, an artificially intelligent creature, a robotic system, and so on.

Further, in some embodiments, the structural data may include each of a three-dimensional human body shape model, a full-body musculoskeletal model, and a full-body anatomy model. Further, the at least one functional data may include physiological data.

Further, in some embodiments, the processing may include identifying the digital twin data amongst a plurality of digital twin data that may be included in the storage device206based on the identifier. Further, the extracting may include decrypting at least one encrypted data block based on the at least one key.

Further, in some embodiments, the processing device204may be configured for determining a template model data associated with a plurality of individuals. Further, the processing device204may be configured for individualizing the template model data based on personal data of the individual. Further, the processing device204may be configured for generating the digital twin data based on the individualizing. Further, the digital twin data may include a human digital twin data.

Further, in some embodiments, the individualizing may include performing at least one data transformation of the template model data. Further, the at least one data transformation may include at least one of fitting, morphing, and scaling.

Further, in some embodiments, the personal data may include at least one of a two-dimensional image of the individual, a three-dimensional image or scan of the individual, a motion capture data, a magnetic resonance imaging (MRI) data and a computed tomography (CT) data.

In further embodiments, the system200may include a plurality of sensors302-304(as shown inFIG. 3) communicatively coupled to the processing device204. Further, the plurality of sensors302-304may be configured to generate sensor data corresponding to a plurality of characteristics of the individual. Further, the plurality of characteristics corresponds to at least two of the at least one structure, the at least one function and the at least one behavior. Further, the processing device204may be configured for analyzing the sensor data. Further, the processing device204may be configured for updating the digital twin data based on the analyzing of the sensor data.

Further, in some embodiments, the template model data may include a muscular-skeleton model, a full body anatomy model, and a body shape model. Further, the individualizing may include generating a first unified model using the muscular-skeleton model and the full body anatomy model based on identification of shared data of bone structure and joint centers as reference. Further, the at least one data transformation may include scaling. Further, the individualizing may include generating a second unified model using the full body anatomy model and the body shape model based on shared data of body surface as reference. Further, the at least one data transformation may include morphing of the second unified model. Further, the individualizing may include generating a third unified model using the body shape model and the muscular-skeleton model based on shared data of landmarks as reference. Further, the at least one data transformation may include fitting the third unified model.

Further, in some embodiments, the communication device202may be configured for receiving a plurality of additional data associated with the individual from a plurality of additional data sources. Further, the generating of the digital twin data may be based on the additional data. Further, the plurality of additional data may include personal medical records, personal medical test reports, and wearable sensors, and so on.

Further, in some embodiments, the template model data may include a muscular-skeleton model, a full body anatomy model, and a body shape model. Further, the individualizing may include performing at least one of reconstructing the body shape model from the two-dimensional image using deep learning and reconstructing the body shape model by fitting a template body shape model with the three-dimensional image. Further, the individualizing may include deriving each of the muscular-skeleton model and the full body anatomy model based on the body shape model.

Further, in some embodiments, the template model data may include a muscular-skeleton model, a full body anatomy model, and a body shape model. Further, the individualizing may include reconstructing the full body anatomy model by fitting a template model with the CT data. Further, the individualizing may include deriving each of the body shape model and the muscular-skeleton model based on the full body anatomy model.

In some embodiments, a computer server may be used by a HDT vendor. Therefore, the system200is characterized to include the communication device202(for communicating with client devices), the processing device204and the storage device206.

FIG. 3is a block diagram of the system200for provisioning the digital data of the individual, in accordance with some embodiments.

FIG. 4is a flowchart of a method400for provisioning digital data of an individual, in accordance with some embodiments. Accordingly, at402, the method400may include receiving, using a communication device, a request from a business provider device associated with a business provider. Further, the request may include an identifier associated with the individual and at least one key associated with at least one data block of a digital twin data associated with the individual. Further, the digital twin data may include structural data representing at least one structure of the individual, at least one functional data representing at least one function of the individual, and behavioral data representing at least one behavior of the individual.

Further, at404, the method400may include processing, using a processing device, the request.

Further, at406, the method400may include retrieving, using a storage device, the at least one data block.

Further, at408, the method400may include extracting, using the processing device, the at least one data block based on the processing.

Further, at410, the method400may include transmitting, using the communication device, the at least one data block of the digital twin data to the business provider device. Further, the business provider device may be configured to use the at least one data block to personalize at least one of a product and a service provisioned to the individual.

Further, in some embodiments, the individual may include a human being. In some embodiments, the individual may include a non-human being, such as, for example, an animal, an artificially intelligent creature, a robotic system, and so on.

Further, in some embodiments, the processing may include identifying the digital twin data amongst a plurality of digital twin data that may be included in the storage device based on the identifier. Further, the extracting may include decrypting at least one encrypted data block based on the at least one key.

Further, in some embodiments, the structural data comprises each of a three-dimensional human body shape model, a full-body musculoskeletal model, and a full-body anatomy model, wherein the at least one functional data comprises physiological data.

FIG. 5is a flowchart of a method500for generating the digital twin data, in accordance with some embodiments. Further, at502, the method500may include determining, using the processing device, a template model data associated with a plurality of individuals.

Further, at504, the method500may include individualizing, using the processing device, the template model data based on personal data of the individual.

Further, at506, the method500may include generating, using the processing device, the digital twin data based on the individualizing. Further, the digital twin data comprises a human digital twin data.

Further, in some embodiments, the individualizing may include performing at least one data transformation of the template model data. Further, the at least one data transformation may include at least one of fitting, morphing, and scaling.

Further, in some embodiments, the personal data may include at least one of a two-dimensional image of the individual, a three-dimensional image or scan of the individual, a motion capture data, a magnetic resonance imaging (MRI) data, and a computed tomography (CT) data.

In further embodiments, the method500may include receiving, using the communication device, a plurality of additional data associated with the individual from a plurality of additional data sources. Further, the generating of the digital twin data may be based on the additional data. Further, the plurality of additional data may include personal medical records, personal medical test reports, and data from wearable sensors, and so on.

FIG. 6is a flowchart of a method600for updating the digital twin data, in accordance with some embodiments. Accordingly, at602, the method600may include generating, using a plurality of sensors communicatively coupled to the processing device, sensor data corresponding to a plurality of characteristics of the individual. Further, the plurality of characteristics corresponds to at least two of the at least one structure, the at least one function, and the at least one behavior.

Further, at604, the method600may include analyzing, using the processing device, the sensor data.

Further, at606, the method600may include updating, using the processing device, the digital twin data based on the analyzing of the sensor data.

FIG. 7is a flowchart of a method700for the individualizing the template model data, in accordance with some embodiments. Accordingly, the template model data may include a muscular-skeleton model, a full body anatomy model, and a body shape model. Further, at702, the method700may include generating a first unified model using the muscular-skeleton model and the full body anatomy model based on identification of shared data of bone structure and joint centers as reference. Further, the at least one data transformation may include scaling of the first unified model.

Further, at704, the method700may include generating a second unified model using the full body anatomy model and the body shape model based on shared data of body surface as reference. Further, the at least one data transformation may include morphing of the second unified model.

Further, at706, the method700may include generating a third unified model using the body shape model and the muscular-skeleton model based on shared data of landmarks as reference. Further, the at least one data transformation may include fitting the third unified model.

FIG. 8is a flowchart of a method800for individualizing the template model data, in accordance with some embodiments. Accordingly, the template model data may include a muscular-skeleton model, a full body anatomy model, and a body shape model. Further, at802, the method800may include performing at least one of reconstructing the body shape model from the two-dimensional image using deep learning and reconstructing the body shape model by fitting a template body shape model with the three-dimensional image.

Further, at804, the method800may include deriving each of the muscular-skeleton model and the full body anatomy model based on the body shape model.

FIG. 9is a flowchart of a method900for individualizing the template model data, in accordance with some embodiments. Accordingly, the template model data may include a muscular-skeleton model, a full body anatomy model, and a body shape model. Further, at902, the method900may include reconstructing the full body anatomy model by fitting a template model with the CT data.

Further, at904, the method900may include deriving each of the body shape model and the muscular-skeleton model based on the full body anatomy model.

FIG. 10is a flow diagram of a method1000for facilitating generating of a human digital twin, in accordance with some embodiments. Accordingly, the method1000may include a body shape model1002. Further, the method1000may include a musculoskeletal model1004. Further, the method1000may include a full body Finite Element (FE) model1006. Further, the method1000may include other body models1008. Further, the body shape mode1002may provide a complete 3D surface mesh description of human body shape along with anthropometric measurements. Further, the musculoskeletal model1004may include bony geometry, rigid linkage with multiple degrees of freedom to define joint kinematics, and Hill-type models of muscles and tendons and provides a non-invasive means to study human kinematics and movement. Further, the full body FE model (or full-body anatomy FE model)1006may use solid FE meshes to describe the complete anatomical structure of the human body in terms of tissue groups. Further, the other body models1008may include a human behavior model, a lifestyle model, and a cognitive model that may be integrated. Further, at1010, the method1000may include unification of model structure (such as the body shape model1002, the musculoskeletal model1004, the full body FE model1006, and other body models1008) and data structure. Further, at1012, the method1000may include a step of individualization. Further, the individualization may be performed using fitting, morphing, and scaling. Further, the method1000may include personal data1018. Further, the personal data1018may include but are not limited to a 2D imagery, a 3D body scan, a motion capture data, a magnetic resonance imaging (MRI) data, and a computed tomography (CT) data. Further, the personal data1018may be categorized in terms of physical, biological, and physiological features/characteristics. Further, the personal data1018may be categorized in terms of static or dynamic. Further, the personal data1018may be categorized in terms of discrete or continuous (sequential). Further, the personal data1018may be categorized in terms of 1-D, 2-D, or 3-D. Further, the personal data1018may be categorized in terms of images, videos, signals, digital records, and text records. Further, the method1000may include individualized and unified digital human models1014. Further, the individualized and unified digital human models1014may represent human anthropometric and biomechanical features and characteristics. Further, data containers associated with the individualized and unified digital human models1014may be used to store and manage personal physical, biological, and physiological data, health data, activity data, and medical data, and many other types of data. Further, at1016, the method1000may include a step of generation of the human digital twin.

Further, if the personal data1018provided is 2D images and/or videos, a computer vision and deep learning-based method may be used to reconstruct a 3D shape model (such as the body shape model1002) from 2D imagery. Further, the musculoskeletal model1004and the full body FE model1006may be derived from the body shape model1002.

Further, if the personal data1018provided is a 3D body scan, a template body shape model may be fitted into the 3D body scan to get the body shape model1002. Further, the musculoskeletal model1004and the full body anatomy FE model1006may be derived from the body shape model1002.

Further, if the personal data1018provided is a CT scan, the full body anatomy FE model1006may be derived from the CT scan by fitting a template model to the CT scan. Further, the body shape model1002and the musculoskeletal model1004may be derived from the full body anatomy FE model1006.

Further, a human body shape (with anthropometry), muscular-skeleton, and full body anatomical structure may be described separately by three independent models (such as the body shape model1002, the musculoskeletal model1004, and the full body FE model1006) of a different type. However, human features/characteristics are inter-dependent and interactive. Further, the body shape model1002, the musculoskeletal model1004, and the full body FE model1006may be associated with human physical features and characteristics. Further, the body shape model1002, the musculoskeletal model1004, and the full body FE model1006describing specific aspects of human, may be integrated into a framework with a unified model structure (such as the individualized and unified digital human models1014) and data structure to share common data and to facilitate information exchange. The unified model structure and data structure may remain the same for every individual.

FIG. 11is a flow diagram of a method1100for facilitating the unification of model structures, in accordance with some embodiments. Accordingly, the model structures may include a body shape model1102, a muscular-skeleton model1104, and a full body anatomy Finite Element (FE) model. Further, at1108, the method1100may include the unification of the muscular-skeleton model1104with the full body anatomy FE model1106using the shared data of bone structure and joint centers as the reference and may be achieved through scaling. Further, at1110, the method1100may include unification of the full body anatomy FE model1106with the body shape model1102using shared data of body surface/skin as the reference and may be achieved through morphing. Further, at1112, the method1100may include unification of the body shape model1102with the muscular-skeleton model1104using the shared data of landmarks as the reference and may be achieved through fitting.

FIG. 12is a flow diagram of a Human Digital Twin (HDT) data management system1200, in accordance with some embodiments. Accordingly, some human features or status may not be suitable to be represented by a model or need to be updated frequently or monitored in real-time. Further, the HDT data management system1200(such as the system200) may be configured for receiving data from various sources that may include, but are not limited to, medical records1202(or personal medical records), medical test reports1204(or personal medical test reports), and wearable sensors1206put on an individual. Further, at1208, the HDT data management system1200may be configured for transmitting the data into data containers such as physical data1210, biological data1212, and physiological data1214. Further, the HDT data management system1200may be configured for storing and managing the data to a data server1216.

FIG. 13is a block diagram showing an HDT vendor1302, a human individual1304, a business1306, and a Human Digital Twin (HDT)1308, in accordance with some embodiments. Accordingly, the HDT vendor1302may build, maintain, and update the HDT1308for the human individual1304. Further, the human individual1304may be a customer to the HDT vendor1302. Further, the human individual1304may own the HDT1308and control the access and use of the data contained. When the business1306intends to provide individualized or personalized products/services to the human individual1304, the business1306may request the human individual1304, that may be a customer to the business1306, to authorize the access and use of a specific data block from the HDT1308that may be required for the products/services. Further, the business1306may provide furnishing tools for the human individual1304to use the Human Digital Twin (HDT)1308. If the human individual1304approves the request, the human individual1304may provide a unique key to the business1306for the specific data block. Further, the business1306may submit the key to the HDT vendor1302to get the specific data block requested.

With reference toFIG. 14, a system consistent with an embodiment of the disclosure may include a computing device or cloud service, such as computing device1400. In a basic configuration, computing device1400may include at least one processing unit1402and a system memory1404. Depending on the configuration and type of computing device, system memory1404may comprise, but is not limited to, volatile (e.g. random-access memory (RAM)), non-volatile (e.g. read-only memory (ROM)), flash memory, or any combination. System memory1404may include operating system1405, one or more programming modules1406, and may include a program data1407. Operating system1405, for example, may be suitable for controlling computing device1400's operation. In one embodiment, programming modules1406may include image-processing module, machine learning module. Furthermore, embodiments of the disclosure may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated inFIG. 14by those components within a dashed line1408.

Computing device1400may have additional features or functionality. For example, computing device1400may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated inFIG. 14by a removable storage1409and a non-removable storage1410. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. System memory1404, removable storage1409, and non-removable storage1410are all computer storage media examples (i.e., memory storage.) Computer storage media may include, but is not limited to, RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device1400. Any such computer storage media may be part of device1400. Computing device1400may also have input device(s)1412such as a keyboard, a mouse, a pen, a sound input device, a touch input device, a location sensor, a camera, a biometric sensor, etc. Output device(s)1414such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used.

As stated above, a number of program modules and data files may be stored in system memory1404, including operating system1405. While executing on processing unit1402, programming modules1406(e.g., application1420) may perform processes including, for example, one or more stages of methods, algorithms, systems, applications, servers, databases as described above. The aforementioned process is an example, and processing unit1402may perform other processes. Other programming modules that may be used in accordance with embodiments of the present disclosure may include machine learning applications.