An example of a system for programming neurostimulation according to a stimulation configuration may include stimulation configuration circuitry, volume definition circuitry, stimulation effect circuitry, and recording circuitry. The stimulation configuration circuitry may be configured to determine the stimulation configuration. The volume definition circuitry may be configured to determine stimulation field model(s) (SFM(s)) each representing a volume of tissue activated by the neurostimulation. The stimulation effect circuitry may be configured to determine a stimulation effect type for each tagging point specified for the SFM(s) and to tag the SFM(s) at each tagging point with the stimulation effect type determined for that tagging point. The stimulation effect type for each tagging point is a type of stimulation resulting from the neurostimulation as measured at that tagging point. The recording circuitry may be configured to generate SFM data representing the determined SFM(s) with the stimulation effect type tagged at each tagging point.

An example of a system for programming neurostimulation according to a stimulation configuration may include stimulation configuration circuitry, volume definition circuitry, stimulation effect circuitry, and recording circuitry. The stimulation configuration circuitry may be configured to determine the stimulation configuration. The volume definition circuitry may be configured to determine stimulation field model(s) (SFM(s)) each representing a volume of tissue activated by the neurostimulation. The stimulation effect circuitry may be configured to determine a stimulation effect type for each tagging point specified for the SFM(s) and to tag the SFM(s) at each tagging point with the stimulation effect type determined for that tagging point. The stimulation effect type for each tagging point is a type of stimulation resulting from the neurostimulation as measured at that tagging point. The recording circuitry may be configured to generate SFM data representing the determined SFM(s) with the stimulation effect type tagged at each tagging point.

A system creates an audio profile. The audio profile may be stored in a database. For example, the audio profile may be securely stored in a database of a social network and associated with a user account. The audio profile may contain data describing the way in which the specific user hears and interprets sounds. Systems and applications which present sounds to the user may access the audio profile and modify the sounds presented to the user based on the data in the audio profile to enhance the audio experience for the user.

A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.

A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.

A data transfer apparatus (“DTA”) connects to the field generator in a TTFields therapy system using the same connector on the field generator that is used to connect a transducer interface to the field generator. The field generator automatically determines whether the transducer interface or the DTA is connected to it. When the transducer interface is connected to the field generator, the field generator operates to deliver TTFields therapy to a patient. On the other hand, when the DTA is connected to the field generator, the field generator transfers patient-treatment data to the DTA, and the DTA accepts the data from the field generator. After the field generator and the DTA have been disconnected, the DTA transmits the data to a remote server, e.g., via the Internet or via cellular data transmission.

A knee brace comprises a medial hinge device and a lateral hinge device each including an upper arm configured to be attached to a thigh, and a lower arm configured to be attached to a shank. An assembly joins free ends of the upper arm and of the lower arm in each of the medial hinge device and the lateral hinge device, the assembly of each of the medial hinge device and the lateral hinge device including an operative set of pivot and pivot slot, and another operative set of follower and at least one follower slot. The assemblies of the medial hinge device and a lateral hinge are configured to induce a corrective constraint on leg movement. A system for generating a knee brace customized to a patient is also provided.

A knee brace comprises a medial hinge device and a lateral hinge device each including an upper arm configured to be attached to a thigh, and a lower arm configured to be attached to a shank. An assembly joins free ends of the upper arm and of the lower arm in each of the medial hinge device and the lateral hinge device, the assembly of each of the medial hinge device and the lateral hinge device including an operative set of pivot and pivot slot, and another operative set of follower and at least one follower slot. The assemblies of the medial hinge device and a lateral hinge are configured to induce a corrective constraint on leg movement. A system for generating a knee brace customized to a patient is also provided.

Systems and methods may be used to predict an applicable a hearing assistance device shell or model. For example, a method may include obtaining patient information, determining, using a machine learning trained model, a correlation between an input vector and each of a plurality of feature vectors corresponding to a plurality of hearing assistance device models, and ranking the plurality of hearing assistance device models based on respective correlations to the input vector. Information corresponding to a highest ranked hearing assistance device model may be output.

The present disclosure discloses a system (100) to accurately determine the calorie consumed during daily activities/exer cise of a user associated with a user device (102) having a plurality of sensors. The system (100) comprises a rules repository (104), an input module (106), a user repository (108), a monitoring module (110), a first analysis module (112), a second analysis module (114), a third analysis module (116) and a calorie consumption module (118). The rules repository (104) stores three sets of pre-determined calculating rules and analysis rules. The input module (106) enables user to enter a plurality of primary user details and store it in the user repository (108). The monitoring module (110) receive a plurality of dynamic user data, a surroundings data and a user activity data using the sensors. The received data along with the primary user details is analysed. A first result, a second result and a third result are calculated post analysis and are added to get a final calorie result.