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 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: 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 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.

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 may include a wearable device configured to be worn on a wrist of a user. The wearable device ma 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.

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 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.

Abstract: 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.