Abstract: Electrodes that can be used to determine a first type of physiological signal and a second type of physiological signal are disclosed. The first type of physiological signal can be electromyography (EMG) signals produced by activity of muscles and tendons. Identification of hand movements can be supplemented with pose detection circuitry such as inertial measurement units (IMUs). These EMG signals can be processed to identify hand movements and recognize gestures associated with those hand movements. The second type of physiological signal can be electromyography (ECG) signals produced by the heart. The electrodes can be used to detect EMG and ECG signals dependent upon a current configuration of sensing circuitry. The electrodes can be used to determine a measurement quality associated with the EMG signals.

Abstract: Aspects of the subject technology provide improved gesture detection including collection of data from multiple sensors into a data structure that may be analyzed to estimate a plurality of gesture inferences, and then the plurality of gesture inferences may be integrated into a detected gesture for the period of time. Analysis of the data package may be performed by separate machine learning models, each model producing a corresponding gesture inference.

Abstract: Electrodes that can be formed in a flexible band of a wrist-worn device to detect hand gestures are disclosed. Multiple rows of electrodes can be configured to detect electromyography (EMG) signals produced by activity of muscles and tendons. The band can include removable electrical connections (e.g., pogo pins) to enable the electrode signals to be routed to processing circuitry in the housing of the wrist-worn device. Measurements between signals from the active electrodes and one or more reference electrodes can be obtained to capture EMG signals at a number of locations on the band. The measurement method and mode of operation (lower power coarse detection or higher power fine detection) can determine the location and number of electrodes to be measured. These EMG signals can be processed to identify hand movements and recognize gestures associated with those hand movements.

Abstract: A computer system detects a gesture, such as an air gesture, and performs an operation based on the gesture.

Abstract: Electrodes that can be formed in a flexible band of a wrist-worn device to detect hand gestures are disclosed. Multiple rows of electrodes can be configured to detect electromyography (EMG) signals produced by activity of muscles and tendons. The band can include removable electrical connections (e.g., pogo pins) to enable the electrode signals to be routed to processing circuitry in the housing of the wrist-worn device. Measurements between signals from the active electrodes and one or more reference electrodes can be obtained to capture EMG signals at a number of locations on the band. The measurement method and mode of operation (lower power coarse detection or higher power fine detection) can determine the location and number of electrodes to be measured. These EMG signals can be processed to identify hand movements and recognize gestures associated with those hand movements.

Abstract: Aspects of the subject technology provide improved techniques for estimating muscular force. The improved techniques may include single-channel or multiple-channel surface electromyography (EMG), such as via a measurement device worn on a wrist. A muscular force estimate may be based on one or more measurements of variation between adjacent voltage measurements and estimates of spectral properties of the voltage measurements. The resulting muscular force estimate may for a basis for improved hand gesture recognition and/or heath metrics of the user.

Abstract: Aspects of the subject technology relate to providing gesture-based control of electronic devices. Providing gesture-based control may include determining, with a machine learning system that includes multiple machine learning models, a prediction of one or more gestures and their corresponding probabilities of being performed. A likelihood of the user's intent to actually perform that gesture may then be generated, based on the prediction and a gesture detection factor. The likelihood may be dynamically updated over time, and a visual, auditory, and/or haptic indicator of the likelihood may be provided as user feedback. The visual, auditory, and/or haptic indicator may be helpful to guide the user to the correct gesture if the gesture is intended, or to stop performing an action similar to the gesture if the gesture is not intended.

Abstract: Electrodes that can be formed in a flexible band of a wrist-worn device to detect hand gestures are disclosed. Multiple rows of electrodes can be configured to detect electromyography (EMG) signals produced by activity of muscles and tendons. The band can include removable electrical connections (e.g., pogo pins) to enable the electrode signals to be routed to processing circuitry in the housing of the wrist-worn device. Measurements between signals from the active electrodes and one or more reference electrodes can be obtained to capture EMG signals at a number of locations on the band. The measurement method and mode of operation (lower power coarse detection or higher power fine detection) can determine the location and number of electrodes to be measured. These EMG signals can be processed to identify hand movements and recognize gestures associated with those hand movements.

Abstract: Aspects of the subject technology relate to gesture-control inputs to an electronic device for controlling one or more other devices. The electronic device can efficiently provide gesture control for multiple other devices by mapping a finite set of user gestures to a specific set of gesture-control elements for each of the multiple other devices. In this way a single gesture can be detected for potentially controlling various different functions of various different devices. Prior to gesture control, the electronic device may receive a selection of a particular one of the multiple other devices for control, and obtain the specific set of gesture-control elements for gesture control of that selected device.

Abstract: The present application relates to apparatus, systems, and methods to perform subject-aware self-supervised learning of a machine-learning model for classification of data, such as classification of biosignals.