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.

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: 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: 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: 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 gesture-based control are provided. A system may include a wearable device comprising a plurality of biopotential channels and a location sensor. The system may be configured to generate a data stream and classify a gesture based on an analysis of an analytical segment. The gesture classification may include (a) determining that a first portion of the analytical segment shows a signal parameter at a baseline; (b) determining that a second portion of the analytical segment shows the signal parameter satisfying a first threshold condition; (c) determining that a third portion of the analytical segment shows the signal parameter satisfying a second threshold condition; (d) determining that a time between the second portion and the third portion is greater than a threshold; and (e) while the signal parameter satisfies the first and/or second threshold condition, detecting a change in location of the wearable device.

Abstract: Systems and methods for gesture-based control are disclosed. In some embodiments, a system may include a wearable device configured to obtain physiological data and wrist location data. The system may be configured to detect a first gesture and transition to an object control state in which changes in the wrist location data and/or the physiological data are configured to control a movement of the object. In the object control state, the system may be configured to detect a first motion of a body part, generate a first command to move the object in a first command direction, detect a second motion of the body part, and generate a command to move the object in a second command direction. The system may detect a second gesture indicating an intention to cease control of the object, and transition out of the object control state.

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 control are disclosed. In some embodiments, a system may include a plurality of electrode pairs, a motion sensor, a controller, and a classifier. The system may be configured to: enter a monitoring state in which the system is configured to receive data; receive a first set of data; determine that the first set of data does not satisfy one or more action criteria; return to the monitoring state without transmitting the first set of data to the classifier; receive a second set of data; determine that the second set of data satisfies the one or more action criteria; transmit the second set of data to the classifier; using the classifier, analyze the second set of data to generate an interpreted output indicating a gesture performed by the person; and based on the interpreted output, generate a machine instruction.

Abstract: Systems and methods for position-based gesture control are described. In some embodiments, a system may include a wearable device including a biopotential sensor and a location sensor. The system may be configured to determine that a body part is in a first position relative to a person's body, detect a first gesture, and, in response, direct a first instruction to a first destination. The system may be further configured to determine that a body part is in a second position relative to the person's body, detect a second gesture, and, in response, direct a second instruction to a second destination different than the first destination.

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: Systems and methods for gesture-based control are disclosed. In some embodiments, a system may include a wearable device configured to obtain physiological data and wrist location data. The system may be configured to detect a first gesture and transition to an object control state in which changes in the wrist location data and/or the physiological data are configured to control a movement of the object. In the object control state, the system may be configured to detect a first motion of a body part, generate a first command to move the object in a first command direction, detect a second motion of the body part, and generate a command to move the object in a second command direction. The system may detect a second gesture indicating an intention to cease control of the object, and transition out of the object control state.

Abstract: Among other things, a user interface device has a sensor configured to detect, at a wrist of a human, nerve or other tissue electrical signals associated with an intended contraction of a muscle to cause a rapid motion of a finger. An output provides information representative of the nerve or other tissue electrical signals associated with the intended contraction of the muscle to an interpreter of the information.