A system including a medical device is provided. The medical device includes at least one sensor configured to acquire first data descriptive of a patient, first memory storing a plurality of templates, and at least one processor coupled to the at least one sensor and the first memory. The at least one processor is configured to identify a first template of the plurality of templates that is similar to the first data, to determine first difference data based on the first template and the first data, and to store the first difference data in association with the first template. The system may further include the programmable device.

A system including a medical device is provided. The medical device includes at least one sensor configured to acquire first data descriptive of a patient, first memory storing a plurality of templates, and at least one processor coupled to the at least one sensor and the first memory. The at least one processor is configured to identify a first template of the plurality of templates that is similar to the first data, to determine first difference data based on the first template and the first data, and to store the first difference data in association with the first template. The system may further include the programmable device.

A system for controlling blood pressure includes a wearable interface having an internal contact surface, the wearable interface configured to at least partially encircle a first portion of a first limb of a subject, a sensing module carried by the wearable interface and configured to determine at least a change in blood pressure of the first limb of the subject, and an energy application module carried by the wearable interface and configured to apply energy of two or more types to the first limb of the subject.

Systems, methods and computer-readable media are provided for identifying persons who are likely to benefit from a neurosurgical procedure, such as lobectomy, hemispherectomy, lesionectomy, callosotomy, and the like. Measured values of physiological variables may be combined via a multi-variable predictive model. In some embodiments, this may take the form of a multinomial logistic regression classifier. In other embodiments, the evidence-combining may be implemented via a neural network or support vector machine or similar machine learning or classification methods. In an embodiment, a leading indicator of near-term responsiveness to the regimen may be provided to a caregiver, such as a neurologist, and further may be integrated with case-management software and an electronic health record decision-support system.

Systems, methods and computer-readable media are provided for identifying persons who are likely to benefit from a neurosurgical procedure, such as lobectomy, hemispherectomy, lesionectomy, callosotomy, and the like. Measured values of physiological variables may be combined via a multi-variable predictive model. In some embodiments, this may take the form of a multinomial logistic regression classifier. In other embodiments, the evidence-combining may be implemented via a neural network or support vector machine or similar machine learning or classification methods. In an embodiment, a leading indicator of near-term responsiveness to the regimen may be provided to a caregiver, such as a neurologist, and further may be integrated with case-management software and an electronic health record decision-support system.

A training apparatus has an input device and a wearable computing device with a bio-signal sensor and a display to provide an interactive virtual reality (“VR”) environment for a user. The bio-signal sensor receives bio-signal data from the user. The user interacts with content that is presented in the VR environment. The user interactions and bio-signal data are scored with a user state score and a performance scored. Feedback is given to the user based on the scores in furtherance of training. The feedback may update the VR environment and may trigger additional VR events to continue training.

A training apparatus has an input device and a wearable computing device with a bio-signal sensor and a display to provide an interactive virtual reality (“VR”) environment for a user. The bio-signal sensor receives bio-signal data from the user. The user interacts with content that is presented in the VR environment. The user interactions and bio-signal data are scored with a user state score and a performance scored. Feedback is given to the user based on the scores in furtherance of training. The feedback may update the VR environment and may trigger additional VR events to continue training.

A lower limb rehabilitation system based on augmented reality and a brain computer interface includes a display, a plurality of motion sensors, a brain wave monitor, and an analysis platform. The display is configured to receive and play a virtual scene video to guide a user to perform gait rehabilitation training. The plurality of motion sensors is configured to sense gait data. The brain wave monitor is configured to record an electroencephalogram signal by detecting an electric current change in a brain wave of the user. The analysis platform is configured to compare the gait data with the virtual scene video to determine the accuracy of footsteps of the user and provide feedback. The analysis platform inputs the electroencephalogram signal to a machine learning model to quantify the electroencephalogram signal into an index value representing a lower limb motor function of the user.

A lower limb rehabilitation system based on augmented reality and a brain computer interface includes a display, a plurality of motion sensors, a brain wave monitor, and an analysis platform. The display is configured to receive and play a virtual scene video to guide a user to perform gait rehabilitation training. The plurality of motion sensors is configured to sense gait data. The brain wave monitor is configured to record an electroencephalogram signal by detecting an electric current change in a brain wave of the user. The analysis platform is configured to compare the gait data with the virtual scene video to determine the accuracy of footsteps of the user and provide feedback. The analysis platform inputs the electroencephalogram signal to a machine learning model to quantify the electroencephalogram signal into an index value representing a lower limb motor function of the user.

An electronic apparatus according to various embodiments of the present disclosure may comprise: a sensor unit for sensing the movement of the electronic apparatus; a communication unit for communicating with an external apparatus; an input unit for receiving a user input; an output unit for providing information to a user; and a control unit for outputting a message inducing a particular action to the user through the output unit on the basis of medical information of the user and the movement of the electronic apparatus, or controlling the communication unit to transmit information related to the electronic apparatus to an external apparatus.