Abstract: Methods, systems, and devices for operating a wearable device are described. The wearable device may determine that a user is wearing the wearable device. The wearable device may collect physiological data from the user based on determining that the user is wearing the wearable device. The wearable device may then determine an identity of the user based at least in part on the collected physiological data matching a physiological profile associated with the identity. The wearable device may then authenticate the user for an action by the wearable device that requires user authentication.

Abstract: Methods, systems, and devices for gesture recognition are described. A system may identify geographical location data associated with a user throughout a time interval and may identify a set of gesture profiles corresponding to a set of gestures associated with the geographical location data of the user. The system may additionally acquire physiological data, including motion data, associated with the user from a wearable device worn by the user and may identify a set of motion segments within the time interval based on the motion data. Additionally, the system may identify a gesture the user engaged in based on matching a motion segment of the set of motion segments to a gesture profile of the set of gesture profiles and may cause a graphical user interface (GUI) of a user device running the application to display an indication of the gesture.

Abstract: Methods, systems, and devices for operating a wearable device are described. The wearable device may determine that a user is wearing the wearable device. The wearable device may collect physiological data from the user based on determining that the user is wearing the wearable device. The wearable device may then determine an identity of the user based at least in part on the collected physiological data matching a physiological profile associated with the identity. The wearable device may then authenticate the user for an action by the wearable device that requires user authentication.

Abstract: Methods, systems, and devices for activity tracking are described. A method for automatic activity detection may include receiving physiological data associated with a user from a wearable device and identifying an activity segment during which the user is engaged in a physical activity based on the physiological data. The method may further include identifying location data associated with the user for at least a portion of the activity segment and identifying one or more parameters associated with the physical activity based on the physiological data and the location data. The method may further include causing a user device to display the one or more parameters associated with the physical activity.

Abstract: Methods, systems, and devices for activity tracking are described. A method for automatic activity detection may include receiving physiological data associated with a user from a wearable device and identifying an activity segment during which the user is engaged in a physical activity based on the physiological data. The method may further include identifying location data associated with the user for at least a portion of the activity segment and identifying one or more parameters associated with the physical activity based on the physiological data and the location data. The method may further include causing a user device to display the one or more parameters associated with the physical activity.

Abstract: Methods, systems, and devices for gesture recognition are described. A system may identify geographical location data associated with a user throughout a time interval and may identify a set of gesture profiles corresponding to a set of gestures associated with the geographical location data of the user. The system may additionally acquire physiological data, including motion data, associated with the user from a wearable device worn by the user and may identify a set of motion segments within the time interval based on the motion data. Additionally, the system may identify a gesture the user engaged in based on matching a motion segment of the set of motion segments to a gesture profile of the set of gesture profiles and may cause a graphical user interface (GUI) of a user device running the application to display an indication of the gesture.

Abstract: Methods, systems, and devices for taggable event detection are described. A system may receive geographical location data associated with a user throughout a time interval, and receive physiological data associated with the user from a wearable device. The system may correlate the physiological data with candidate taggable events, where the candidate taggable events are associated with respective confidence values that indicate confidence levels that the corresponding candidate taggable events occurred within the time interval. The system may selectively modify the confidence values associated with the candidate taggable events based on the geographical location data to generate one or more modified confidence values, and identify a taggable event within the time interval based on a modified confidence value associated with the taggable event satisfying a threshold confidence value.

Abstract: Methods, systems, and devices for operating a wearable device are described. The wearable device may determine that a user is wearing the wearable device. The wearable device may collect physiological data from the user based on determining that the user is wearing the wearable device. The wearable device may then determine an identity of the user based at least in part on the collected physiological data matching a physiological profile associated with the identity. The wearable device may then authenticate the user for an action by the wearable device that requires user authentication.

Abstract: Methods, systems, and devices for activity tracking are described. A method for automatic activity detection may include receiving physiological data associated with a user from a wearable device and identifying an activity segment during which the user is engaged in a physical activity based on the physiological data. The method may further include identifying location data associated with the user for at least a portion of the activity segment and identifying one or more parameters associated with the physical activity based on the physiological data and the location data. The method may further include causing a user device to display the one or more parameters associated with the physical activity.

Abstract: Methods, systems, and devices for activity classification are described. A system may receive physiological data associated with a user via a wearable device, where the physiological data includes at least motion data. The system may identify an activity segment during which the user is engaged in a physical activity based on the motion data, where the activity segment is associated with activity segment data including at least the physiological data collected during the activity segment. The system may generate activity classification data associated with the activity segment based on the activity segment data, the activity classification data including a set of classified activity types and corresponding confidence values. The system may then cause a graphical user interface (GUI) of a user device to display the activity segment data and at least one classified activity type of the set of classified activity types.

Abstract: In one embodiment, sensor signals corresponding to motions of a client device are received. Activities of a user corresponding to the client device are determined, based on a first analysis algorithm that uses the sensor signals to select activity types, each activity type corresponding to a particular time period. A reference to the selected activity types is sent to the client device. User input indicating whether the reference is correct is received from the client device. Based on the indication, which reflects the accuracy of the first analysis algorithm, a priority between the first analysis algorithm and a second analysis algorithm is determined, based on determining which analysis algorithm is more accurate.

Abstract: In one embodiment, sensor signals corresponding to motions of a client device are received. Activities of a user corresponding to the client device are determined, based on a first analysis algorithm that uses the sensor signals to select activity types, each activity type corresponding to a particular time period. A reference to the selected activity types is sent to the client device. User input indicating whether the reference is correct is received from the client device. Based on the indication, which reflects the accuracy of the first analysis algorithm, a priority between the first analysis algorithm and a second analysis algorithm is determined, based on determining which analysis algorithm is more accurate.

Abstract: In one embodiment, sensor signals corresponding to motions of a computing device are determined. Activities of a user corresponding to the computing device are determined by selecting activity types which are based on the sensor signals, a set of user characteristics associated with the user, a classification of the set of user characteristics, and signal parameters. A reference to the selected activity types is displayed to the user. User input indicating whether the reference is correct is received. Based on the user input, the signal parameters may be adjusted.

Abstract: In one embodiment, sensor signals corresponding to motions of a computing device are determined. Activities of a user corresponding to the computing device are determined by selecting activity types which are based on the sensor signals, a set of user characteristics associated with the user, a classification of the set of user characteristics, and signal parameters. A reference to the selected activity types is displayed to the user. User input indicating whether the reference is correct is received. Based on the user input, the signal parameters may be adjusted.

Abstract: In one embodiment, sensor signals corresponding to motions of a client computing device are received. The sensor signals are associated with activity periods, each activity period also being associated with a geographic location of the client computing device. For each activity period, one or more activity types are determined for the client computing device based on the sensor signals for the activity period and one or more signal parameters. A reference of the activity type and activity period is sent to the client computing device, and an indication is received regarding whether that reference is correct. The signal parameters may be adjusted based on the received indication.

Abstract: The present invention relates to a microbial composition which is tailored based on the spectrum of microbes found more frequently from the intestine of non-secretor individuals than from the intestine of secretor individuals. The present invention further relates to a method of tailoring a microbial composition based on the spectrum of microbes found more frequently from the intestine of non-secretor individuals than from that of secretor blood group status. The present invention relates to use of the secretor status of an individual as a criterion for microbial supplementation tailored based on the differences in the spectra of microbes found between secretor and non-secretor individuals.

Abstract: In one embodiment, sensor signals corresponding to motions of a client computing device are received. The sensor signals are associated with activity periods, each activity period also being associated with a geographic location of the client computing device. For each activity period, one or more activity types are determined for the client computing device based on the sensor signals for the activity period and one or more signal parameters. A reference of the activity type and activity period is sent to the client computing device, and an indication is received regarding whether that reference is correct. The signal parameters may be adjusted based on the received indication.

Abstract: The production of human retinal pigment epithelial cells having a step of incubating human pluripotent stem cells with binder molecules binding to terminal N-acetyllactosamine (Gal?1-4Glc NAc) and/or blood group H determinant type 2 (Fuc?1-2Gal?1-4Glc NAc). A method of using lectin ECA in a culture of human pluripotent stem cells in order to support the stem cells to differentiate into retinal pigment epithelial cells is also disclosed.

Abstract: The present invention relates to a hematopoietic stem cell and/or a population thereof having a specific profile of cell surface proteins and/or proteoglycans. The present invention also relates to use of proteolytic enzymes, such as pronase and pronase-like enzymes in the modification of the cell surface of a hematopoietic stem cell. The present invention further relates to a method of modifying the cell surface of a hematopoietic stem cell by treatment with proteolytic enzymes, such as pronase and pronase-like enzymes.

Abstract: The present disclosure provides a system for tracking and recording movements of a communication device. The mobile communication device is operable to communicate one or more sensor signals to the system, wherein the sensor signals are indicative of motion associated with activities to which the user exposes the mobile communication device. The system includes a computing hardware that analyses sensor signals to classify them into one or more temporal zones, determines a most likely activity type associated with a given temporal zone, and sends the information regarding most likely activity to the mobile communication device. The user of the mobile communication device provides their confirmation whether the most likely activity type represents a correct analysis, and communicate the confirmation back to the computing hardware for amending parameters and/or algorithms which execute analysis of the sensor signals to improve their accuracy.