Abstract: Hair shine compositions are disclosed. In embodiments, a hair shine composition may include greater than or equal to 50 wt/wt % and less than or equal to 95 wt/wt % water; greater than or equal to 0.5 wt/wt % and less than or equal to 3 wt/wt % propanediol; and greater than or equal to 0.25 wt/wt % and less than or equal to 2 wt/wt % dimethyl isosorbide. In embodiments, a wt/wt % ratio of propanediol to dimethyl isosorbide may be from 1.00:1 to 3.00:1.

Abstract: Systems and methods are proposed for verifying whether a digital twin of abiological asset (or biological system) of a subject (110) indeed relates to the subject (110). Such proposals may be based on obtaining: a first signal comprising a value of a physiological parameter of the subject (e.g. determined by a physical and/or virtual sensor arrangement); and a second signal generated by the digital twin that simulates a value of a physiological parameter of the subject (110). By comparing the first and second signals, a verification result may then be determined.

Abstract: The invention implements a digital twin to provide e.g. estimated measurements of one or more physiological or anatomical parameters in a way that requires less local storage space and local processing capacity. Embodiments are based on constructing and implementing a lean digital twin model or dataset by storing results of a number of full digital model simulations in advance and then using these pre-stored simulation results (42) to provide physiological parameter estimations, in place of the full digital twin model (32).

Abstract: A provided method is for configuring settings of a computing device for providing more efficient and reliable acquisition of data for use in updating a personalized digital model (digital twin) of a subject. The method comprises configuring settings of a biometric authentication function of a computing device so as to provide for overlap in the input data requirements of the biometric authentication function and the input data requirements of the digital twin. There may be different authentication protocols available at the computing device, each requiring different input sensing data. Based on knowledge of these different authentication protocols and data requirements, and based on knowledge of data input needs of a digital twin, an authentication protocol can be selected and/or its settings adjusted, so that when performing biometric authentication, the same acquired sensor data can also be used for deriving physiological parameter data of the subject, for updating the digital twin.

Abstract: A computer system is disclosed comprising a processor arrangement communicatively coupled to a data storage arrangement storing a digital model of a section of a lumen system of a patient; and a communication module communicatively coupled to said processor arrangement and arranged to receive sensor data pertaining to an internal parameter of said lumen system from a sensor within said section of the lumen system. The processor arrangement is arranged to receive said sensor data from the communication module; retrieve said digital model from the data storage arrangement; simulate an actual physical condition of said lumen system by developing said digital model based on the received sensor data; and generate an output relating to said simulated actual physical condition for updating an electronic device. Also disclosed is a method of monitoring a patient with such a computer system, a computer program product for implementing such a method and a patient monitoring system.

Abstract: Systems and methods are proposed for training and employing a machine learning algorithm for identifying uncertain Digital Twin model parameters. Such proposals may make use of a set of DTs that is chosen such that it comprises DTs that have achieved a target/predetermined morbidity scenario. The training may then identify uncertain model parameters for the set of DTs. Using the identified uncertain model parameters, the set of DTs may then be run so as to assess whether the target morbidity scenario is achieved.

Abstract: A system (10) configures operating settings and parameters of a patient monitor (14), in particular a bedside patient monitor, based on use of a digital twin (32) of the at least one patient (13) being monitored. The system (10) configures settings of a signal processing module (16) of the patient monitor, the signal processing module adapted to process incoming sensor data to extract or derive one or more clinical parameters relating to the patient. Outputs of the digital twin relate to a current or predicted future state of the patient and thus can intelligently guide the signal processing module to extract and output clinical parameters which are most clinically relevant to the patient's condition at any given time. Thus the patient monitor (14) automatically displays the most clinically relevant parameters for the patient at each given moment, which allows a clinician to rapidly ascertain the clinical state of the patient, and to take any necessary action quickly to treat the patient.

Abstract: A system (10) for configuring data collection for provision to a digital twin for generating a desired set of output information (e.g. physiological or anatomical parameter estimations) from the digital twin. The system is configured to detect available sources (56) of physical sensor data (58) pertaining to a patient, and to compare these with determined input data requirements of a digital twin (32) of the patient for computing a particular defined set of required output information. Depending on the result, a virtual sensing module (28) can be used to generate supplementary ‘virtual’ sensor data to compensate for any insufficiencies in the available physical sensor data or augment the physical sensor data, and the system can re-configure operating settings of the available physical sensors (56) to optimally meet the input data requirements of the digital model.

Abstract: A system (10) for configuring operation settings of a medical device (16) in a state of interaction with a patient (14), and determining features or options of a patient treatment, is based on use of co-operative feedback between digital models (34, 32) for the medical device (16) on the one hand and at least a portion of the anatomy with which the medical device is interaction on the other. A feedback loop is implemented between outputs (46, 48) and inputs (42, 44) of the two models, so that outputs of each model form at least part of the input provided to the other of the models. This way a decision outcome regarding optimum operation settings for the medical device, for balancing both medical aims and also operational efficiency aims for the medical device, can effectively be arrived at iteratively through recursive circular feedback between the two models. The decision outcome may be refined though this process of back and forth feedback.

Abstract: Systems and methods are proposed for controlling a Digital Twin of a biological asset of a subject based on: (i) characteristics of a user of the DT; (ii) a status of the subject; and (iii) previous usage of the DT. Such control is facilitated through the generation of one or more parameter values for the DT. For instance, the parameter value(s) may define one or more elements or components of the DT, such as program code, input requirements, output characteristics or a value of a tunable element of the DT.

Abstract: Disclosed is a method (100) of providing a personalized parameter model of at least part of the physiology of a person (10) carrying a set of sensors (12, 14, 16) with a computer system (20, 50) comprising a processor arrangement (22, 52) for the purpose of updating a biophysical model or digital twin of the person. The method comprises, with said processor arrangement, receiving (103) sensor data from said set of sensors with the communication interface over a defined period of time, the sensor data from each sensor representing a parameter relevant to an actual physiological state of the person; evaluating (105) the received sensor data from each sensor to define the personalized parameter model (3), said model including the evaluated dynamic behaviour of the values of each parameter observed with said sensors over the defined period of time and providing (107) the defined personalized parameter model for updating a digital model (1) of at least part of the anatomy of said person.

Abstract: An approach is proposed for managing human operators (22), the approach comprising managing power delivery to sensor components (24) carried by the human operators based on a need for the human operators for performing a task. The human operators (e.g. clinical staff in a hospital) carry sensors for monitoring location and physiological parameters of the staff. An operation mode of the sensors (24) is altered (e.g. the sensors (24) are activated) based on resource request signals received indicative of need for a particular kind of human assistance. Out of the human operators (22) capable of providing the needed kind of human assistance, one or more human operators are selected based on received sensor data from the sensor sets (24) relating to the location and the physiological parameters.

Abstract: Systems and methods are proposed for assessing digital twin accuracy by processing first and second input data to generated target output data and supplementary output data, respectively. Based on the target output data and the supplementary output data, a value of accuracy of the DT may be determined.

Abstract: Hair shine compositions are disclosed. In embodiments, a hair shine composition may include greater than or equal to 50 wt/wt % and less than or equal to 95 wt/wt % water; greater than or equal to 0.5 wt/wt % and less than or equal to 3 wt/wt % propanediol; and greater than or equal to 0.25 wt/wt % and less than or equal to 2 wt/wt % dimethyl isosorbide. In embodiments, a wt/wt % ratio of propanediol to dimethyl isosorbide may be from 1.00:1 to 3.00:1.

Abstract: It is proposed to combine a multi-organ in-vitro system with a multi-organ in-silico system to provide improved modelling/prediction of multi-organ interaction(s) for a subject. By way of example, a subject-specific multi-organ in-vitro system may undergo perturbation. The response of the other organ(s) to the perturbation modelled by the in-vitro model may then be identified, and this response may then be used in the in-silico model to predict the outcome(s) of the perturbation(s).

Abstract: Proposed are methods and systems for generating a prediction for a first organ of a subject using a Digital Twin model of a second, different organ of the subject.

Abstract: A physiological parameter sensing system receives signals from (on includes) a first physiological parameter sensor and an activity monitor. The first physiological parameter signal is used to determine when additional physiological parameter sensing is recommended and an alert is then generated. The alert has characteristics which depend on the type and/or level of activity currently being undertaken by the user. In this way, the alert can be selected so that it is perceived by the user but without being an annoyance.

Abstract: There a proposed concepts for predicting the expiration date, or validity period, of a subject-specific digital twin. By calculating an expiration time of a digital twin model for a subject, proposed embodiments may enable an understanding of when digital twin predictions remain valid. In this way, it may be determined when the acquisition of data is desirable or required. Improved data collection may therefore be supported by proposed embodiments.

Abstract: A method and system for determining an amount of risk an air pollutant poses to a subject. A risk level is calculated by determining a dosage of the air pollutant, received by the subject over a predetermined period of time, and obtaining historic information about historic dosages of the air pollutant. The risk level is then calculated using the dosage and the historic information to thereby determine a risk level associated with the accumulated deposition of the air pollutant in the portion of the subject's respiratory tract.

Abstract: Embodiments are described herein for selecting and applying models of digital twins for various purposes. In various embodiments, one or more user needs of a user seeking to utilize a digital twin may be identified. One or more situational needs may be identified of a subject simulated by the digital twin. The one or more situational needs may be identified based on data obtained from the subject. Based on one or more of the user needs and one or more of the situational needs of the subject, one or more models of the digital twin may be selected and applied to generate digital twin output that simulates one or more aspects of the subject.