Augmented reality systems and methods for user health analysis. Methods for user health analysis may include collecting data for an initial prediction model and continuing to collect additional data based on one or more data criteria. The methods may further include updating the initial prediction model based on the additional data to produce a revised prediction model or causing an intervention to occur based on the additional data. The data may be collected by a display system including one or more sensors configured to collect user-specific data and a display device configured to present virtual content to a user. The display device may be configured to output light with variable wavefront divergence.

An intelligent surgical tool control system, comprising a tool management system; an indicating means to indicate at least one surgical event; a communicable database for storing, for each item of interest, its identity, its present 3D position and at least one previous 3D position; and at least one processor to identify, from a surgical event, an output surgical procedure. The tool management system can comprise a maneuvering mechanism to maneuver a surgical tool in at least two dimensions; and a controller to control at least one of activation and deactivation of a surgical tool and articulation of a surgical tool. The indicating means can indicate a surgical event selected from movement of a moving element and presence of an item of interest, where movement is determinable if the current 3D position of the moving element is substantially different from a previous 3D position of the same.

A multiple sensor-fusing based interactive training system, including a posture sensor, a sensing module, a computing module, and a display module, is provided. The posture sensor is configured to sense posture data and myoelectric data related to a training action. The sensing module is configured to output limb torque data according to the posture data, and output muscle group activation time data according to the myoelectric data. The computing module is configured to respectively convert the limb torque data and the muscle group activation time data into a moment-skeleton coordinate system and a muscle strength eigenvalue-skeleton coordinate system according to a skeleton coordinate system, perform fusion calculation, calculate evaluation data based on a result of the fusion calculation, and judge that the training action corresponds to a known exercise action according to the evaluation data. The display module is configured to display the evaluation data and the known exercise action.

A wearable sensor configuration that is configured to operate in a communications system and method is provided. The wearable sensor configuration includes at least one sensor unit being arranged to detect signals from activity of muscles of a user. Thus, the wearable sensor configuration includes at least one sensor unit that is arranged to detect signals from muscle activity of a user of the communications system, such as for example, signals generated by movements and/or muscle activity of a user of the communications system. The wearable sensor configuration can also include a first electronic arrangement that is arranged to determine Internal Digital Information based on detected signals by means of a first mapping function. The Internal Digital Information can also be transmitted to a second electronic arrangement. The Internal Digital Information may be transmitted wirelessly.

In a method of neurological assessment, multichannel electromyography (EMG) data are acquired for an anatomical region. A pairwise EMG channel-EMG channel similarity matrix is generated from the acquired multichannel EMG data. Network analysis is performed on the similarity matrix to generate a network representing the similarity matrix. One or more metrics of the network are computed. One or more biomarkers are determined for the anatomical region based on the one or more metrics. In another method, EMG data are acquired using an electrode array contacting skin of a target anatomy, the EMG data are processed to produce reduced-dimensionality data; and time-invariant muscle synergies and corresponding time-varying activation functions are determined in the reduced-dimensionality data.

In a method of neurological assessment, multichannel electromyography (EMG) data are acquired for an anatomical region. A pairwise EMG channel-EMG channel similarity matrix is generated from the acquired multichannel EMG data. Network analysis is performed on the similarity matrix to generate a network representing the similarity matrix. One or more metrics of the network are computed. One or more biomarkers are determined for the anatomical region based on the one or more metrics. In another method, EMG data are acquired using an electrode array contacting skin of a target anatomy, the EMG data are processed to produce reduced-dimensionality data; and time-invariant muscle synergies and corresponding time-varying activation functions are determined in the reduced-dimensionality data.

A method of optimally placing spinal cord stimulator (SCS) leads includes acquiring components of somatosensory evoked potentials (SSEPs), compound action potentials and triggered EMG; analyzing the waveforms; and quantifying waveform features in a single display such that a surgeon can quickly and easily determine optimal placement (as it relates to laterality and level of placement on the spinal cord) of SCS leads in a patient under general anesthesia without additional expert help.

A method of optimally placing spinal cord stimulator (SCS) leads includes acquiring components of somatosensory evoked potentials (SSEPs), compound action potentials and triggered EMG; analyzing the waveforms; and quantifying waveform features in a single display such that a surgeon can quickly and easily determine optimal placement (as it relates to laterality and level of placement on the spinal cord) of SCS leads in a patient under general anesthesia without additional expert help.

A behavioral state of a brain is classified by automatically selecting one or more sensors based on the signals received from each sensor and one or more selection criteria using one or more processors, calculating at least one measured value from the signal(s) of the selected sensor(s), classifying the behavioral state as: (a) an awake state whenever the measured value(s) for the selected sensor(s) is lower than a first threshold value, (b) a sleep state (N2) whenever the measured value(s) for the selected sensor(s) is equal to or greater than the first threshold value and the measured value(s) is not greater than a second threshold value, or (c) a slow wave sleep state (N3) whenever the measured value(s) from the selected sensor(s) is greater than the first threshold value and the measured value(s) is greater than the second threshold value, and providing a notification of the classified behavioral state.

A behavioral state of a brain is classified by automatically selecting one or more sensors based on the signals received from each sensor and one or more selection criteria using one or more processors, calculating at least one measured value from the signal(s) of the selected sensor(s), classifying the behavioral state as: (a) an awake state whenever the measured value(s) for the selected sensor(s) is lower than a first threshold value, (b) a sleep state (N2) whenever the measured value(s) for the selected sensor(s) is equal to or greater than the first threshold value and the measured value(s) is not greater than a second threshold value, or (c) a slow wave sleep state (N3) whenever the measured value(s) from the selected sensor(s) is greater than the first threshold value and the measured value(s) is greater than the second threshold value, and providing a notification of the classified behavioral state.