Abstract: A method of generating a calibration factor is described. In an example, the method may include establishing a wireless communication connection with a fitness machine associated with a physical workout session. The method may also include receiving accrued workout data of the physical workout session and first machine data from the fitness machine. method may also include initiating a workout session on the wearable computer based on the first machine data. During the workout session, the method may also include obtaining sensor data, obtaining second machine data, and determining the calibration factor based on the second machine data and the sensor data. The method may also include determining pre-connection fitness data based on the accrued workout data and the calibration factor. The method may also include generating a workout session summary based on the pre-connection fitness data.

Abstract: A method of generating a calibration factor is described. In an example, the method may include establishing a wireless communication connection with a fitness machine. The method may also include obtaining first machine data from the fitness machine. The first machine data may specify a type of the fitness machine. The method may also include initiating a workout session on the wearable computer based on the first machine data. During the workout session, the method may also include obtaining sensor data, obtaining second machine data, and determining the calibration factor based on the second machine data and the sensor data. The method may also include generating a workout session summary based on the calibration factor.

Abstract: In an embodiment, a method comprises: establishing, by a wireless wearable computer worn by a user, a wireless communication connection with a fitness machine; obtaining machine data from the fitness machine while the user is engaged in a workout session on the fitness machine; obtaining, from a heart rate sensor of the wireless device, heart rate data of the user; determining a calibrated maximal oxygen consumption of the user based on the heart rate data and the machine data; determining a heart rate caloric expenditure based on the heart rate data and the calibrated maximal oxygen consumption of the user; and providing information corresponding to the heart rate caloric expenditure for presentation.

Abstract: In an embodiment, a method comprises: establishing, by a wireless wearable computer worn by a user, a wireless communication connection with a fitness machine; obtaining machine data from the fitness machine while the user is engaged in a workout session on the fitness machine; obtaining, from a heart rate sensor of the wireless device, heart rate data of the user; determining a work rate caloric expenditure by applying a work rate calorie model to the machine data; determining a calibrated maximal oxygen consumption of the user based on the heart rate data and the work rate caloric expenditure; determining a heart rate caloric expenditure by applying a heart rate calorie model to the heart rate data and the calibrated maximal oxygen consumption of the user; and sending to the fitness machine via the communication connection, at least one of the work rate caloric expenditure or the heart rate caloric expenditure.

Abstract: A method of generating a calibration factor is described. In an example, the method may include establishing a wireless communication connection with a fitness machine. The method may also include obtaining first machine data from the fitness machine. The first machine data may specify a type of the fitness machine. The method may also include initiating a workout session on the wearable computer based on the first machine data. During the workout session, the method may also include obtaining sensor data, obtaining second machine data, and determining the calibration factor based on the second machine data and the sensor data. The method may also include generating a workout session summary based on the calibration factor.

Abstract: Embodiments are disclosed for a wearable computer with fitness machine connectivity for improved activity monitoring. In an embodiment, a method comprises: establishing, by a processor of a wireless device, a wireless communication connection with a fitness machine; obtaining, by the processor, machine data from the fitness machine; determining, by the processor, a workout session according to the machine data; initiating the workout session on the wireless device; during the workout session: obtaining, from a sensor of the computing device, physiological data of a user of the fitness machine; determining, by the processor, fitness data for the user based on the physiological data, the machine data and at least one user characteristic; and sending, by the processor, the fitness data to the fitness machine.

Abstract: Embodiments are disclosed for a wearable computer with fitness machine connectivity for improved activity monitoring. In an embodiment, a method comprises: establishing, by a processor of a wireless device, a wireless communication connection with a fitness machine; obtaining, by the processor, machine data from the fitness machine; determining, by the processor, a workout session according to the machine data; initiating the workout session on the wireless device; during the workout session: obtaining, from a sensor of the computing device, physiological data of a user of the fitness machine; determining, by the processor, fitness data for the user based on the physiological data, the machine data and at least one user characteristic; and sending, by the processor, the fitness data to the fitness machine.

Abstract: In an embodiment, a method comprises: establishing, by a wireless wearable computer worn by a user, a wireless communication connection with a fitness machine; obtaining machine data from the fitness machine while the user is engaged in a workout session on the fitness machine; obtaining, from a heart rate sensor of the wireless device, heart rate data of the user; determining a work rate caloric expenditure by applying a work rate calorie model to the machine data; determining a calibrated maximal oxygen consumption of the user based on the heart rate data and the work rate caloric expenditure; determining a heart rate caloric expenditure by applying a heart rate calorie model to the heart rate data and the calibrated maximal oxygen consumption of the user; and sending to the fitness machine via the communication connection, at least one of the work rate caloric expenditure or the heart rate caloric expenditure.

Abstract: Embodiments are disclosed for a wearable computer with fitness machine connectivity for improved activity monitoring. In an embodiment, a method comprises: establishing, by a processor of a wireless device, a wireless communication connection with a fitness machine; obtaining, by the processor, machine data from the fitness machine; determining, by the processor, a workout session according to the machine data; initiating the workout session on the wireless device; during the workout session: obtaining, from a sensor of the computing device, physiological data of a user of the fitness machine; determining, by the processor, fitness data for the user based on the physiological data, the machine data and at least one user characteristic; and sending, by the processor, the fitness data to the fitness machine.

Abstract: Embodiments are disclosed for a wireless wearable computer with fitness machine connectivity for improved activity monitoring using caloric expenditure models.

Abstract: Embodiments are disclosed for a wearable computer with fitness machine connectivity for improved activity monitoring. In an embodiment, a method comprises: establishing, by a processor of a wireless device, a wireless communication connection with a fitness machine; obtaining, by the processor, machine data from the fitness machine; determining, by the processor, a workout session according to the machine data; initiating the workout session on the wireless device; during the workout session: obtaining, from a sensor of the computing device, physiological data of a user of the fitness machine; determining, by the processor, fitness data for the user based on the physiological data, the machine data and at least one user characteristic; and sending, by the processor, the fitness data to the fitness machine.

Abstract: Methods and apparatus for detecting, geo-locating and/or classifying a GPS or GNSS jamming signal are disclosed. One such method comprises the steps of analysing a spectrogram of the jamming signal using a tree-based decision process and as a result, then selecting one of a number of types of jamming signal, wherein one of the decisions (1103) in the tree is whether the spectrogram has spectral periodicity.

Abstract: Embodiments are disclosed for a wireless wearable computer with fitness machine connectivity for improved activity monitoring using caloric expenditure models.

Abstract: Embodiments are disclosed for a wearable computer with fitness machine connectivity for improved activity monitoring. In an embodiment, a method comprises: establishing, by a processor of a wireless device, a wireless communication connection with a fitness machine; obtaining, by the processor, machine data from the fitness machine; determining, by the processor, a workout session according to the machine data; initiating the workout session on the wireless device; during the workout session: obtaining, from a sensor of the computing device, physiological data of a user of the fitness machine; determining, by the processor, fitness data for the user based on the physiological data, the machine data and at least one user characteristic; and sending, by the processor, the fitness data to the fitness machine.

Abstract: Methods and apparatus for detecting, geo-locating and/or classifying a GPS or GNSS jamming signal are disclosed. One such method comprises the steps of analysing a spectrogram of the jamming signal using a tree-based decision process and as a result, then selecting one of a number of types of jamming signal, wherein one of the decisions (1103) in the tree is whether the spectrogram has spectral periodicity.

Abstract: A system is provided for monitoring a relative displacement of a pair of end-winding components. The system includes a structure mounted to the end-winding components. The system further includes a fiber Bragg grating mounted to a non-curved surface of the structure, where the fiber Bragg grating is configured to reflect incident radiation having a peak intensity at a respective wavelength based on a strain of the fiber Bragg grating. The structure is configured so that the strain produced by the structure limits a magnitude of the strain of the fiber Bragg grating within a predetermined range over a span of the relative displacement of the pair of end-winding components.

Abstract: A system is provided for monitoring a relative displacement of a pair of end-winding components. The system includes a structure mounted to the end-winding components. The system further includes a fiber Bragg grating mounted to a non-curved surface of the structure, where the fiber Bragg grating is configured to reflect incident radiation having a peak intensity at a respective wavelength based on a strain of the fiber Bragg grating. The structure is configured so that the strain produced by the structure limits a magnitude of the strain of the fiber Bragg grating within a predetermined range over a span of the relative displacement of the pair of end-winding components.