Abstract: Aspects relate to calculating energy expenditure values from an apparatus configured to be worn on an appendage of a user. Steps counts may be quantified, such as by detecting arm swings peaks and bounce peaks in motion data. A search range of acceleration frequencies related to an expected activity may be established. Frequencies of acceleration data within a search range may be analyzed to identify one or more peaks, such as a bounce peak and an arm swing peak. Novel systems and methods may determine whether to utilize the arm swing data, bounce data, and/or other data or portions of data to quantify steps. The number of peaks (and types of peaks) may be used to choose a step frequency and step magnitude. At least a portion of the motion data may be classified into an activity category based upon the quantification of steps.

Abstract: Aspects relate to calculating energy expenditure values from an apparatus configured to be worn on an appendage of a user. Steps counts may be quantified, such as by detecting arm swings peaks and bounce peaks in motion data. A search range of acceleration frequencies related to an expected activity may be established. Frequencies of acceleration data within a search range may be analyzed to identify one or more peaks, such as a bounce peak and an arm swing peak. Novel systems and methods may determine whether to utilize the arm swing data, bounce data, and/or other data or portions of data to quantify steps. The number of peaks (and types of peaks) may be used to choose a step frequency and step magnitude. At least a portion of the motion data may be classified into an activity category based upon the quantification of steps.

Abstract: Determining pace or speed based on sensor data may include determining an amount of contact time a user's foot has with a workout surface such as the ground. Contact time may be determined by identifying samples in the sensor data that correspond to various events such as a heelstrike, a toe-off and a subsequent heelstrike. In one example, these events may be identified by determining a sequence of three sample values (e.g., a triplet) that exceeds corresponding thresholds. The validity of an identified triplet (e.g., heelstrike, toe-off and heelstrike) may be confirmed by determining whether a difference between a last event sample and a middle event sample is greater than a difference between the middle event sample and an initial event sample. Once confirmed, a contact time may be determined from the triplet. A linear or non-linear relationship may then be applied to the contact time to determine a speed or pace.

Abstract: Determining pace or speed based on sensor data may include determining an amount of contact time a user's foot has with a workout surface such as the ground. Contact time may be determined by identifying samples in the sensor data that correspond to various events such as a heelstrike, a toe-off and a subsequent heelstrike. In one example, these events may be identified by determining a sequence of three sample values (e.g., a triplet) that exceeds corresponding thresholds. The validity of an identified triplet (e.g., heelstrike, toe-off and heelstrike) may be confirmed by determining whether a difference between a last event sample and a middle event sample is greater than a difference between the middle event sample and an initial event sample. Once confirmed, a contact time may be determined from the triplet. A linear or non-linear relationship may then be applied to the contact time to determine a speed or pace.

Abstract: Aspects relate to calculating energy expenditure values from an apparatus configured to be worn on an appendage of a user. Steps counts may be quantified, such as by detecting arm swings peaks and bounce peaks in motion data. A search range of acceleration frequencies related to an expected activity may be established. Frequencies of acceleration data within a search range may be analyzed to identify one or more peaks, such as a bounce peak and an arm swing peak. Novel systems and methods may determine whether to utilize the arm swing data, bounce data, and/or other data or portions of data to quantify steps. The number of peaks (and types of peaks) may be used to choose a step frequency and step magnitude. At least a portion of the motion data may be classified into an activity category based upon the quantification of steps.

Abstract: Determining pace or speed based on sensor data may include determining an amount of contact time a user's foot has with a workout surface such as the ground. Contact time may be determined by identifying samples in the sensor data that correspond to various events such as a heelstrike, a toe-off and a subsequent heelstrike. In one example, these events may be identified by determining a sequence of three sample values (e.g., a triplet) that exceeds corresponding thresholds. The validity of an identified triplet (e.g., heelstrike, toe-off and heelstrike) may be confirmed by determining whether a difference between a last event sample and a middle event sample is greater than a difference between the middle event sample and an initial event sample. Once confirmed, a contact time may be determined from the triplet. A linear or non-linear relationship may then be applied to the contact time to determine a speed or pace.

Abstract: Activities, actions and events during user performance of physical activity may be detected using various algorithms and templates. Templates may include an arrangement of one or more states that may identify particular event types and timing between events. Templates may be specific to a particular type of activity (e.g., types of sports, drills, events, etc.), user, terrain, time of day and the like.

Abstract: Aspects relate to calculating energy expenditure values from an apparatus configured to be worn on an appendage of a user. Steps counts may be quantified, such as by detecting arm swings peaks and bounce peaks in motion data. A search range of acceleration frequencies related to an expected activity may be established. Frequencies of acceleration data within a search range may be analyzed to identify one or more peaks, such as a bounce peak and an arm swing peak. Novel systems and methods may determine whether to utilize the arm swing data, bounce data, and/or other data or portions of data to quantify steps. The number of peaks (and types of peaks) may be used to choose a step frequency and step magnitude. At least a portion of the motion data may be classified into an activity category based upon the quantification of steps.

Abstract: Activities, actions and events during user performance of physical activity may be detected using various algorithms and templates. Templates may include an arrangement of one or more states that may identify particular event types and timing between events. Templates may be specific to a particular type of activity (e.g., types of sports, drills, events, etc.), user, terrain, time of day and the like.

Abstract: Activities, actions and events during user performance of physical activity may be detected using various algorithms and templates. Templates may include an arrangement of one or more states that may identify particular event types and timing between events. Templates may be specific to a particular type of activity (e.g., types of sports, drills, events, etc.), user, terrain, time of day and the like.

Abstract: Activities, actions and events during user performance of physical activity may be detected using various algorithms and templates. Templates may include an arrangement of one or more states that may identify particular event types and timing between events. Templates may be specific to a particular type of activity (e.g., types of sports, drills, events, etc.), user, terrain, time of day and the like.

Abstract: Activities, actions and events during user performance of physical activity may be detected using various algorithms and templates. Templates may include an arrangement of one or more states that may identify particular event types and timing between events. Templates may be specific to a particular type of activity (e.g., types of sports, drills, events, etc.), user, terrain, time of day and the like.

Abstract: Determining pace or speed based on sensor data may include determining an amount of contact time a user's foot has with a workout surface such as the ground. Contact time may be determined by identifying samples in the sensor data that correspond to various events such as a heelstrike, a toe-off and a subsequent heelstrike. In one example, these events may be identified by determining a sequence of three sample values (e.g., a triplet) that exceeds corresponding thresholds. The validity of an identified triplet (e.g., heelstrike, toe-off and heelstrike) may be confirmed by determining whether a difference between a last event sample and a middle event sample is greater than a difference between the middle event sample and an initial event sample. Once confirmed, a contact time may be determined from the triplet. A linear or non-linear relationship may then be applied to the contact time to determine a speed or pace.

Abstract: Activities, actions and events during user performance of physical activity may be detected using various algorithms and templates. Templates may include an arrangement of one or more states that may identify particular event types and timing between events. Templates may be specific to a particular type of activity (e.g., types of sports, drills, events, etc.), user, terrain, time of day and the like.

Abstract: Determining pace or speed based on sensor data may include determining an amount of contact time a user's foot has with a workout surface such as the ground. Contact time may be determined by identifying samples in the sensor data that correspond to various events such as a heelstrike, a toe-off and a subsequent heelstrike. In one example, these events may be identified by determining a sequence of three sample values (e.g., a triplet) that exceeds corresponding thresholds. The validity of an identified triplet (e.g., heelstrike, toe-off and heelstrike) may be confirmed by determining whether a difference between a last event sample and a middle event sample is greater than a difference between the middle event sample and an initial event sample. Once confirmed, a contact time may be determined from the triplet. A linear or non-linear relationship may then be applied to the contact time to determine a speed or pace.

Abstract: Activities, actions and events during user performance of physical activity may be detected using various algorithms and templates. Templates may include an arrangement of one or more states that may identify particular event types and timing between events. Templates may be specific to a particular type of activity (e.g., types of sports, drills, events, etc.), user, terrain, time of day and the like.

Abstract: Compositions and methods for preventing microbial infections are disclosed.

Abstract: Aspects relate to calculating energy expenditure values from an apparatus configured to be worn on an appendage of a user. Steps counts may be quantified, such as by detecting arm swings peaks and bounce peaks in motion data. A search range of acceleration frequencies related to an expected activity may be established. Frequencies of acceleration data within a search range may be analyzed to identify one or more peaks, such as a bounce peak and an arm swing peak. Novel systems and methods may determine whether to utilize the arm swing data, bounce data, and/or other data or portions of data to quantify steps. The number of peaks (and types of peaks) may be used to choose a step frequency and step magnitude. At least a portion of the motion data may be classified into an activity category based upon the quantification of steps.

Abstract: Activities, actions and events during user performance of physical activity may be detected using various algorithms and templates. Templates may include an arrangement of one or more states that may identify particular event types and timing between events. Templates may be specific to a particular type of activity (e.g., types of sports, drills, events, etc.), user, terrain, time of day and the like.

Abstract: Aspects relate to calculating energy expenditure values from an apparatus configured to be worn on an appendage of a user. Steps counts may be quantified, such as by detecting arm swings peaks and bounce peaks in motion data. A search range of acceleration frequencies related to an expected activity may be established. Frequencies of acceleration data within a search range may be analyzed to identify one or more peaks, such as a bounce peak and an arm swing peak. Novel systems and methods may determine whether to utilize the arm swing data, bounce data, and/or other data or portions of data to quantify steps. The number of peaks (and types of peaks) may be used to choose a step frequency and step magnitude. At least a portion of the motion data may be classified into an activity category based upon the quantification of steps.