A wireless power transfer system includes a wireless power transfer device configured to determine a magnetic field, from among a plurality of directionally different potential magnetic fields that the wireless power transfer device is configured to generate, that has, at a receiver coil of an electronic device, a direction aligned with the receiver coil.

A wireless power transfer system includes a wireless power transfer device configured to determine a magnetic field, from among a plurality of directionally different potential magnetic fields that the wireless power transfer device is configured to generate, that has, at a receiver coil of an electronic device, a direction aligned with the receiver coil.

Systems and methods are presented for establishing a communication link between two or more electronic devices. A portable eyewear electronic device is configured to communicate with a handheld electronic device, such as a ring, that in turn is retained by an accessory electronic device to establish a wired communication link. The accessory electronic device may be retained or housed by a second accessory electronic device, such as a remote control or wearable device.

Systems and methods are presented for establishing a communication link between two or more electronic devices. A portable eyewear electronic device is configured to communicate with a handheld electronic device, such as a ring, that in turn is retained by an accessory electronic device to establish a wired communication link. The accessory electronic device may be retained or housed by a second accessory electronic device, such as a remote control or wearable device.

The present disclosure provides a system and method for accurately detecting exercises performed by a user through a combination of signals from a visual input device and from one or more sensors of a wearable device. For each workout type, an algorithm leverages multimodal inputs for automatic workout detection/identification. Using multiple sources of visual and gestural inputs to detect the same workout results in a higher confidence in the detection. Moreover, it allows for continued detection of the workout, including counting repetitions, even when one or more signals becomes unavailable, such as if the user moves out of a field of view of the visual input device.

The present disclosure provides a system and method for accurately detecting exercises performed by a user through a combination of signals from a visual input device and from one or more sensors of a wearable device. For each workout type, an algorithm leverages multimodal inputs for automatic workout detection/identification. Using multiple sources of visual and gestural inputs to detect the same workout results in a higher confidence in the detection. Moreover, it allows for continued detection of the workout, including counting repetitions, even when one or more signals becomes unavailable, such as if the user moves out of a field of view of the visual input device.

A measurement method includes: generating first measurement data based on observation data of an observation point of a structure; generating second measurement data by performing filter processing on the first measurement data; calculating a first deflection amount of the structure; calculating a second deflection amount by performing filter processing on the first deflection amount; approximating the second measurement data with a linear function of the second deflection amount to calculate a first-order coefficient and a zero-order coefficient; calculating a third deflection amount based on the first-order coefficient, the zero-order coefficient, and the second deflection amount; calculating an offset based on the zero-order coefficient, the second deflection amount, and the third deflection amount; and calculating a static response by adding the offset and a product of the first-order coefficient and the first deflection amount.

A measurement method includes: generating first measurement data based on observation data of an observation point of a structure; generating second measurement data by performing filter processing on the first measurement data; calculating a first deflection amount of the structure; calculating a second deflection amount by performing filter processing on the first deflection amount; approximating the second measurement data with a linear function of the second deflection amount to calculate a first-order coefficient and a zero-order coefficient; calculating a third deflection amount based on the first-order coefficient, the zero-order coefficient, and the second deflection amount; calculating an offset based on the zero-order coefficient, the second deflection amount, and the third deflection amount; and calculating a static response by adding the offset and a product of the first-order coefficient and the first deflection amount.

Systems and methods for multiple inertial measurement unit sensor fusion using machine learning are provided herein. In certain embodiments, a system includes inertial sensors that produce inertial measurements, a memory unit that stores a fusion model produced by at least one machine learning algorithm, and a processor that receives inertial measurements, where the processor applies the fusion model to the inertial measurements. The fusion model directs the processor to extract features from the inertial measurements, and to select inertial measurements based on a sensor in the plurality of inertial sensors that produced the inertial measurements. Also, the fusion model directs the processor to apply weights to the selected inertial measurements based on the extracted features, to apply compensation coefficients to the selected inertial measurements, and to fuse the selected inertial measurements into an inertial navigation solution.

Systems and methods for multiple inertial measurement unit sensor fusion using machine learning are provided herein. In certain embodiments, a system includes inertial sensors that produce inertial measurements, a memory unit that stores a fusion model produced by at least one machine learning algorithm, and a processor that receives inertial measurements, where the processor applies the fusion model to the inertial measurements. The fusion model directs the processor to extract features from the inertial measurements, and to select inertial measurements based on a sensor in the plurality of inertial sensors that produced the inertial measurements. Also, the fusion model directs the processor to apply weights to the selected inertial measurements based on the extracted features, to apply compensation coefficients to the selected inertial measurements, and to fuse the selected inertial measurements into an inertial navigation solution.