An illustrative system may include a TDC configured to monitor for an occurrence of a photodetector output pulse during a measurement time window that is within and shorter in duration than a light pulse time period, the photodetector output pulse generated by a photodetector when the photodetector detects a photon from a light pulse having a light pulse time period; a PLL circuit for the TDC and having a PLL feedback period defined by a reference clock, the PLL circuit configured to: output a plurality of fine phase signals and output one or more signals representative of a plurality of feedback divider states during the PLL feedback period; and a precision timing circuit configured to adjust, based on one or more of the fine phase signals and/or the feedback divider states, a temporal position of the measurement time window within the light pulse time period.

A method for rendering computer graphics based on saccade detection is provided. One embodiment of the method includes rendering a computer simulated scene for display to a user, detecting an onset of a saccade that causes saccadic masking in an eye movement of the user viewing the computer simulated scene, and reducing a computing resource used for rendering frames of the computer simulated scene during at least a portion of a duration of the saccade. Systems perform similar steps, and non-transitory computer readable storage mediums each storing one or more computer programs are also provided.

Methods and apparatus for interacting with a tag in a radio target area. More specifically, the present invention relates to methods and systems for monitoring temperature and other environmental conditions in a storage area and displaying environmental conditions as digital content in a user interactive interface based upon energy levels received from a radio target area and content from a sensor generating digital content.

Embodiments of the present disclosure sets forth a computer-implemented method comprising receiving, from at least one sensor, sensor data associated with an environment, computing, based on the sensor data, a cognitive load associated with a user within the environment, computing, based on the sensor data, an affective load associated with an emotional state of the user, determining, based on both the cognitive load at the affective load, an affective-cognitive load, determining, based on the affective-cognitive load, a user readiness state associated with the user, and causing one or more actions to occur based on the user readiness state.

Systems, devices, and methods described herein relate to multi-sensory presentation devices, including virtual reality (VR) devices, visual display devices, sound devices, haptic devices, and other forms of presentation devices, that are configured to present sensory elements, including visual and/or audio scenes, to a user. In some embodiments, one or more sensors including electroencephalography (EEG) sensors and a photoplethysmography (PPG) sensors, e.g., included in a brain-computer interface, can measure physiological data of a user to monitor a state of the user during the presentation of the visual and/or audio scenes. Such systems, devices, and methods can adapt one or more visual and/or audio scenes based on user physiological data, e.g., to control or manage the state of the user.

Quantum sensors may have a size suitable for integration with an extended reality device, such as an augmented reality device or a virtual reality device. When the extended reality device is worn on the head of a user, the quantum sensors can detect magnetoencephalography (MEG) signals from the user's brain. Trained computer models may be used in a recognition algorithm to detect and/or classify particular brain activities. The particular brain activities may then be used to control an extended reality application.

Disclosed are methods, systems and non-transitory computer readable memory for gesture control. For instance, a system may include a wearable device configured to be worn on a portion of an arm of a user. The wearable device may include a plurality of electrodes disposed on an interior of the wearable device and configured to obtain biopotential signals from the user's arm; and a biopotential chip. The biopotential microchip may be configured to output, directly or indirectly, biopotential data, acceleration data, and/or angular rate data, or derivatives thereof (“gesture data”), to a machine learning classifier. The machine learning classifier may be configured to generate, based on the gesture data, a gesture output indicating a gesture performed by the user. In some cases, the plurality of electrodes may include one or more wristband electrodes and/or a plurality of hub electrodes in a hub. In some cases, the hub may be curved.

A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided. The system includes an intelligent bio-signal processing system that is operable to: capture bio-signal data and in addition optionally non-bio-signal data; and analyze the bio-signal data and non-bio-signal data, if any, so as to: extract one or more features related to at least one individual interacting with the biofeedback computer system; classify the individual based on the features by establishing one or more brain wave interaction profiles for the individual for improving the interaction of the individual with the one or more biofeedback computer systems, and initiate the storage of the brain waive interaction profiles to a database; and access one or more machine learning components or processes for further improving the interaction of the individual with the one or more biofeedback computer systems by updating automatically the brain wave interaction profiles based on detecting one or more defined interactions between the individual and the one or more of the biofeedback computer systems. A number of additional system and computer implemented method features are also provided.

A system comprising a plurality of neuromuscular sensors, each of which is configured to record a time-series of neuromuscular signals from a surface of a user's body; and at least one computer hardware processor programmed to perform: applying a source separation technique to the time series of neuromuscular signals recorded by the plurality of neuromuscular sensors to obtain a plurality of neuromuscular source signals and corresponding mixing information; providing features, obtained from the plurality of neuromuscular source signals and/or the corresponding mixing information, as input to a trained statistical classifier and obtaining corresponding output; and identifying, based on the output of the trained statistical classifier, and for each of one or more of the plurality of neuromuscular source signals, an associated set of one or more biological structures.

An information processing apparatus includes a behavior state detection sensor configured to detect behavior state information on a behavior state of a user; a behavior pattern information generation unit configured to generate behavior pattern information in a multidimensional space formed of coordinate axes based on the behavior state information to generate a group of spaces in each of which density of a behavior pattern information group as a collection of pieces of the behavior pattern information exceeds predetermined density; a behavior score calculation unit configured to calculate, as a behavior score, information on a size of the space including the behavior pattern information group; and a behavior pattern identification unit configured to identify, as a behavior pattern of the user, the behavior pattern information group in the space for which a value of the behavior score is equal to or larger than a predetermined value.