Abstract: Systems and methods for biometric analysis are described. In some embodiments, a system may include a wearable device configured to be worn on a portion of an arm of a user while the user sleeps. 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. The system also may include a processor configured to analyze biopotential data derived from the biopotential signals to determine one or more characteristics relating to sleep of the user, and generate an output based on the one or more characteristics relating to the sleep of the user.

Abstract: Systems and methods for detecting a physiological response to neuronal activation include a wearable device configured to be worn on a portion of an arm of a user. The wearable device includes a plurality of electrodes configured to detect biopotential signals from the user's arm and a processor coupled to the plurality of electrodes. The processor is configured to analyze biopotential data derived from the biopotential signals to determine an activation level of the user and generate an output indicating the activation level of the user. The system may include additional sensors such as an inertial measurement unit, a photoplethysmography sensor, a galvanic skin response sensor, a temperature sensor, an electrocardiogram sensor, and an ambient light sensor to provide complementary data for analysis. The processor may classify the activation level, trigger actions based on the determined level, and correlate the activation level with cognitive performance.

Abstract: Systems and methods for biometric analysis are described. In some embodiments, a system may include a wearable device comprising a plurality of electrodes, a gesture control processing circuit, and an ECG processing circuit. A common electrode may be configured to provide signals to both the gesture control processing circuit and the ECG processing circuit. While the system is in a gesture control state, the system may be configured to obtain first biopotential signals using at least the common electrode and convert the first biopotential signals to first biopotential data for use in gesture control. While the system is in the ECG state, the system may be configured to obtain second biopotential signals using at least the common electrode and convert the second biopotential signals to ECG data for use in analyzing a user's heart rhythms.

Abstract: Disclosed are methods, systems and non-transitory computer readable memory for gesture inference. For instance, a first method may include computer vision to train and/or infer gesture inferences. For instance, a second method may include using transformations to data and/or ML models to address inter/intra-session variability of sensor data. For instance, a third method may include using ML model selection to select a ML model to address inter/intra-session variability of sensor data.

Abstract: Systems and methods for gesture control are described. In some embodiments, a system for assessing a physiological state of a user. The system may include the wearable device comprising one or more electrodes configured to be disposed adjacent to an external surface of the skin portion. A first timestamp may be determined. The first timestamp may indicate a first time at which a stimulus is presented to the user. A second timestamp indicating a second time at which the biopotential signals indicate an intention by the user to perform a responsive action may be determined. Based at least on the first timestamp and the second timestamp, determine a subject response time for the user. The subject response time may be compared to a baseline response time for the user to generate an assessment of a physiological state of the user.

Abstract: Systems and methods for gesture control are described. In some embodiments, a system for assessing a physiological state of a user. The system may include the wearable device comprising one or more sensors configured to be disposed adjacent to an external surface of the skin portion. A first timestamp may be determined. The first timestamp may indicate a first time at which a stimulus is presented to the user. A second timestamp, which indicates a second time at which physiological data indicates a responsive action of the user, may be determined. Based at least on the first timestamp and the second timestamp, determine a subject response time for the user. The subject response time may be compared to a baseline response time for the user to generate an assessment of a physiological state of the user.

Abstract: Systems and methods for gesture control are described. In some embodiments, a system for assessing a reaction performance of a user engaging with an interactive digital program based on biopotentials detected at an external surface of a skin portion of the user may be provided. The system may include the wearable device comprising one or more electrodes configured to be disposed adjacent to an external surface of the skin portion. A first timestamp may be determined indicating a first time at which a stimulus condition is presented to the user. A second timestamp may be determined indicating a second time at which the biopotential signals indicate an intention by the user to perform a responsive action in response to the stimulus condition. A subject response time for the user may be determined based at least on the first timestamp and the second timestamp.

Abstract: Systems and methods for gesture-based control are provided. In some embodiments, a system may include a wearable device configured to be worn on a person's wrist. The wearable device may include a plurality of biopotential channels, a location sensor, and a processor. The system may be configured to generate a data stream based on the outputs from the biopotential channels and/or the location sensor. The system may be configured to enter a first state in which the output from the location sensor is processed according to a first set of logical rules. The system may be configured to classify a gesture, and, based on the gesture classification, transition to a second state in which the output from the location sensor is processed according to a second set of logical rules that is different than the first set of logical rules.

Abstract: System and methods for gesture-based control are described. In some embodiments, a system may include a wearable device having a biopotential sensor and a wrist motion sensor. The biopotential sensor may be configured to output a first data stream indicating actions of a person's hand. The system may further include a second device configured to output a second data stream, which may also indicate the actions of the person's hand. The system may be configured to analyze the first and second data streams to train a machine learning interpreter to classify actions of a person's hand based on at least biopotential data.

Abstract: Disclosed are methods, systems and non-transitory computer readable memory for gesture inference. For instance, a first method may include computer vision to train and/or infer gesture inferences. For instance, a second method may include using transformations to data and/or ML models to address inter/intra-session variability of sensor data. For instance, a third method may include using ML model selection to select a ML model to address inter/intra-session variability of sensor data.

Abstract: Disclosed are methods, systems and non-transitory computer readable memory for gesture inference. For instance, a first method may include computer vision to train and/or infer gesture inferences. For instance, a second method may include using transformations to data and/or ML models to address inter/intra-session variability of sensor data. For instance, a third method may include using ML model selection to select a ML model to address inter/intra-session variability of sensor data.

Abstract: Disclosed are methods, systems and non-transitory computer readable memory for gesture inference. For instance, a first method may include computer vision to train and/or infer gesture inferences. For instance, a second method may include using transformations to data and/or ML models to address inter/intra-session variability of sensor data. For instance, a third method may include using ML model selection to select a ML model to address inter/intra-session variability of sensor data.

Abstract: Systems and methods for gesture control are described. In some embodiments, a system for assessing a physiological state of a user. The system may include the wearable device comprising one or more sensors configured to be disposed adjacent to an external surface of the skin portion. A first timestamp may be determined. The first timestamp may indicate a first time at which a stimulus is presented to the user. A second timestamp, which indicates a second time at which physiological data indicates a responsive action of the user, may be determined. Based at least on the first timestamp and the second timestamp, determine a subject response time for the user. The subject response time may be compared to a baseline response time for the user to generate an assessment of a physiological state of the user.

Abstract: Systems and methods for gesture control are described. In some embodiments, a system for assessing a physiological state of a user. The system may include the wearable device comprising one or more electrodes configured to be disposed adjacent to an external surface of the skin portion. A first timestamp may be determined. The first timestamp may indicate a first time at which a stimulus is presented to the user. A second timestamp indicating a second time at which the biopotential signals indicate an intention by the user to perform a responsive action may be determined. Based at least on the first timestamp and the second timestamp, determine a subject response time for the user. The subject response time may be compared to a baseline response time for the user to generate an assessment of a physiological state of the user.

Abstract: Systems and methods for gesture-based control are provided. A system may include a wearable device configured to be worn on a wrist of a person and comprising one or more sensors and a display. The wearable device being configured to obtain at least physiological data being configured to indicate a state of a person's hand and wrist location data comprising information relating to a position of the person's wrist. The system may be configured to transition between states in response to gestures detected based on the physiological data and/or the wrist location data.

Abstract: Systems and methods for assessing a neurological condition of a user includes detectors positioned at a surface of a skin of a wrist of a hand of the user. The detectors include a first pair of detectors configured to output a first output and a second pair of detectors configured to output a second output based on detected electrical potentials. Circuitry is configured to reduce amplitudes indicative of frequency components characteristic of ambient noise from the first output and the second output, generate data values based upon the first output and the second output, extract features from the data values including at least a feature based on relative amplitudes between the first output and the second output, and analyze the extracted features to assess the neurological condition of the user.

Abstract: System and methods for gesture-based control are described. In some embodiments, a system may include a wearable device having a biopotential sensor and a wrist motion sensor. The biopotential sensor may be configured to output a first data stream indicating actions of a person's hand. The system may further include a second device configured to output a second data stream, which may also indicate the actions of the person's hand. The system may be configured to analyze the first and second data streams to train a machine learning interpreter to classify actions of a person's hand based on at least biopotential data.

Abstract: Systems and methods for gesture-based control are provided. In some embodiments, a system may include a wearable device configured to be worn on a person's wrist. The wearable device may include a plurality of biopotential channels, a location sensor, and a processor. The system may be configured to generate a data stream based on the outputs from the biopotential channels and/or the location sensor. The system may be configured to enter a first state in which the output from the location sensor is processed according to a first set of logical rules. The system may be configured to classify a gesture, and, based on the gesture classification, transition to a second state in which the output from the location sensor is processed according to a second set of logical rules that is different than the first set of logical rules.

Abstract: Systems and methods for biometric analysis are described. In some embodiments, a system may include a wearable device comprising a plurality of electrodes, a gesture control processing circuit, and an ECG processing circuit. A common electrode may be configured to provide signals to both the gesture control processing circuit and the ECG processing circuit. While the system is in a gesture control state, the system may be configured to obtain first biopotential signals using at least the common electrode and convert the first biopotential signals to first biopotential data for use in gesture control. While the system is in the ECG state, the system may be configured to obtain second biopotential signals using at least the common electrode and convert the second biopotential signals to ECG data for use in analyzing a user's heart rhythms.

Abstract: Systems and methods for gesture control are described. In some embodiments, a system may include a wearable device configured to be worn on a wrist of a user. The wearable device may include a hub and a wristband. The hub may include a plurality of hub electrodes and a biopotential microchip. The wristband may include one or more wristband electrodes and one or more wristband conductors, which may electrically connecting the one or more wristband electrodes to the electrical port of a sealed housing of the hub, which may in turn electrically connect the one or more wristband electrodes to inputs of the biopotential microchip.