A unified coil assembly for magnetic resonance imaging is disclosed. The coil assembly includes an RF coil element and a shim coil array with a shim coil element. The shim coil element is physically separated or partially separated from the RF coil element. The shim coil element includes a DC current loop having a DC power supply connection to allow DC current to generate a local BO magnetic field. The unified coil array assembly is configured to simultaneously provide an RF mode for at least one of transmit or receive and a direct current mode to generate a local B0 magnetic field for B0 shimming Larger number of shim coils relative to the RF coil element provides superior shimming performance. The mutual inductance between the shim coil element and the RF coil element is minimized by proposed geometrical decoupling methods in order to minimize the RF interaction between the two.

A unified coil assembly for magnetic resonance imaging is disclosed. The coil assembly includes an RF coil element and a shim coil array with a shim coil element. The shim coil element is physically separated or partially separated from the RF coil element. The shim coil element includes a DC current loop having a DC power supply connection to allow DC current to generate a local BO magnetic field. The unified coil array assembly is configured to simultaneously provide an RF mode for at least one of transmit or receive and a direct current mode to generate a local BO magnetic field for BO shimming. Larger number of shim coils relative to the RF coil element provides superior shimming performance. The mutual inductance between the shim coil element and the RF coil element is minimized by proposed geometrical decoupling methods in order to minimize the RF interaction between the two.

A method includes collecting reference motion data in a device from a motion sensor worn by a user for a movement having a predetermined classification. The motion sensor is attached to a limb having a joint. A user library entry is generated in the device based on the reference motion data and the predetermined classification. Additional motion data is collected in the device from the motion sensor. User motions in the additional motion data corresponding to the user library entry are classified in the device. Range of motion data associated with the user motions is generated in the device. A report is generated in the device including the user motions and the associated range of motion data.

A bone density estimating method, comprising: acquiring, by an MR scanning device, a magnetic resonance, MR, sequence of a body portion, wherein the MR sequence comprises quantitative information of the body portion; generating, by a processing circuit, an MR image of the body portion based on the MR sequence, wherein each voxel of the MR image represents a volume of the body portion; identifying, by the processing circuit, a part of the MR image representing a bone portion of the body portion; for a voxel of the identified part of the MR image, estimating a bone density of a volume of the bone portion represented by the voxel, based on a quantitative value of the voxel. The quantitative information of the body portion comprises a proton density.

Methods for evaluating subjects having conditions associated with loss of muscle function (e.g., a motor neuron disease, a neuromuscular disease, or a myopathy) by measuring muscle function (e.g., muscle strength) are disclosed.

A method of determining a likelihood of presence of at least one type of arthritis in a hand of a patient is provided, the method comprising capturing an image of a hand of the patient, processing the hand image to determine at least one first predictive value indicative of presence or absence of arthritis in the hand based on presence or absence of a plurality of identifiable hand features in the hand image, the identifiable hand features including visible physical hand features that are usable to diagnose arthritis in the hand, receiving patient information from the patient, the patient information comprising a plurality of responses to a plurality of respective questions that are relevant to diagnosing arthritis in the hand, and determining a likelihood of presence of at least one type of arthritis in the hand using the at least one first predictive value and the patient information.

A method includes collecting reference motion data in a device from a motion sensor worn by a user for a movement having a predetermined classification. The motion sensor is attached to a limb having a joint. A user library entry is generated in the device based on the reference motion data and the predetermined classification. Additional motion data is collected in the device from the motion sensor. User motions in the additional motion data corresponding to the user library entry are classified in the device. Range of motion data associated with the user motions is generated in the device. A report is generated in the device including the user motions and the associated range of motion data.

Smart apparel for monitoring athletics and associated systems and methods are disclosed. An example apparatus includes a data interface to access first motion data and second motion data generated by the smart apparel, the first motion data associated with a first joint on a body and the second motion data associated with a second joint on the body; a motion data fuser to fuse the first motion data and the second motion data; an analytics determiner to process the fused first and second motion data to identify a progression of a motion based activity; and a display organizer to generate a graphical display representing the progression of the motion based activity.

An apparatus comprising at least one sensor to detect the position and/or orientation of a body portion of a subject, the sensor in communication with a computing device to process sensor data and optionally a transmitter to transmit sensor data between the sensor and the computing device and/or one or more computing devices.

There is provided a biological signal processing apparatus. The biological signal processing apparatus includes a biological signal extraction unit (2) configured to extract a biological signal from an electrocardiographic waveform measured by an electrocardiograph (1), an averaging processing unit (3) configured to calculate averaged data using time-series data of the biological signals extracted by the biological signal extraction unit (2), an abnormal value determination unit (4) configured to determine, for each data, whether the data of the biological signal extracted by the biological signal extraction unit (2) is appropriate, based on the averaged data calculated using the data of the biological signals that have occurred before the data, and an abnormal value processing unit (5) configured to perform one of deletion and interpolation of the data of the biological signal determined to be inappropriate by the abnormal value determination unit (4).