Measurement of an induced shear wave in tensioned tissue of a given individual is provided to a machine learning system trained to determine absolute load from shear wave signal data. The machine learning system uses a teaching set linking shear wave signal data to absolute load, however, does not require normal calibration data based on measured loads allowing reduced or no calibration for absolute load determinations.

A deep tendon reflex-eliciting device actuated by pressure against a patient's skin, which releases a spring-loaded mass that delivers an impulse through a fully-enclosed housing. This device includes a weight contained within the casings, and a mainspring in communication with the weight. The mainspring has a bias toward expansion. In the compressed position, the mainspring is also compressed, and the weight is pushed backwards into the rear casing. The weight is released to be driven forward by the mainspring. The weight strikes the inside of the forward casing, delivering an impulse to a surface against Which the device is pressed. A reset spring can push apart the forward and rear casings to reset the device to its expanded position. A case screw is also included which is able to consistently set the impact force of the device.

The tensioning device for tendons comprises: a main body of elongated conformation having at one ending part a positioning portion comprising a pointer element adapted to be positioned onto a bone portion; hooking means to an injured tendon portion; measuring means operatively connected to the hooking means and adapted to detect a tension value at the bone portion.

A brace configured for attachment to a joint of a subject is provided. The brace includes a first arm having a first end and a second end. The brace includes a second arm having a first end and a second end. The brace includes a hinge assembly coupling the first end of the first arm with the first end of the second arm such that the first arm and the second arm are movable to different relative angular orientations. The brace includes a potentiometer coupled to the hinge assembly. A method of monitoring a relative angular orientation of a first arm of a brace relative to a second arm of the brace is also provided. The method includes monitoring an output of a potentiometer coupled to one of the first arm and the second arm.

There is provided an apparatus (100) for detecting tissue inflammation. The apparatus (100) comprises a processor (102) configured to acquire, from at least one sensor (104), a plurality of photoplethysmography, PPG, signals indicative of light detected in a region of tissue at a plurality of respective locations within the region. The processor is also configured to process the acquired plurality of PPG signals to determine an amplitude and a phase of each of the plurality of PPG signals and detect tissue inflammation based on the determined amplitude and phase of each of the plurality of PPG signals.

A method for modeling soft tissue includes receiving one or more images showing an anatomical geometry of a first subject. The anatomical geometry includes a soft tissue. The method also includes measuring a plurality of parameters of the anatomical geometry of the first subject using one or more sensors attached to the first subject. The method also includes receiving a first set of material properties for the soft tissue of the first subject, a second subject, or both. The method also includes identifying a second set of material properties that characterizes the soft tissue while the first subject performs a task. The method also includes determining a strain on the soft tissue, a stress on the soft tissue, or both based at least partially upon the one or more images, the parameters, the first set of material properties, and the second set of material properties.

Segmentation of bony regions in MRI images of joints is automated using a two-stage process. In a first stage, a machine-learning image-slice categorizer is used to categorize image slices of the MRI image data as belonging to one of a set of image-slice categories, depending on presence or absence of bone and/or tendon in the image slice. In a second stage, a first instance of a machine-learning segmentation classifier is used to segment image slices that contain both bone and tendon into bone and non-bone regions, and a second instance of a machine-learning segmentation classifier is used to segment image slices that contain bone but not tendon into bone regions and non-bone regions. Results from the two segmentation classifiers can be combined across image slices to provide a final segmentation of the bony structures, including inflammatory regions, in the image data.

Provided is a non-invasive system and method of determining pennation angle and/or fascicle length based on image processing. An ultrasound scan image is processed to facilitate distinguishing of muscle fiber and tendon. The processed ultrasound scan image is then analyzed. The pennation angle and/or fascicle length is determined based on the analysis. An example method includes receiving an ultrasound scan image of at least a portion of a skin layer as disposed above one or more additional tissue layers, the image provided by a plurality of pixels. The method continues by introducing noise into the pixels of the image and thresholding the pixels of the image to provide a binary image having a plurality of structural elements of different sizes. The method continues with morphing the structural elements of the binary image to remove small structural elements and connect large structural elements. With this resulting image, the method distinguishes muscle fiber and tendon from remaining elements and determines the pennation angle and/or the fascicle length from the muscle fiber and the tendon. Associated apparatuses and computer program products are also disclosed.

The present invention relates to enhancing mechanical properties of tissue such as collagenous or collagen-containing or elastin-containing tissue (e.g., tendons, ligaments, and cartilage) and treating related musculoskeletal and non-musculoskeletal conditions or injuries.

There is disclosed a system and method for measuring the diameter or thickness of a tendon. In an embodiment, the device includes an adjustable tube having a variably sized cross-sectional diameter in a direction perpendicular to an axis extending between the first end and the second end, the adjustable tube being positionable from an open configuration in which the variably sized cross-sectional diameter is larger than a diameter of a tendon so as to allow loading of the tendon, to a closed configuration in which the variably sized cross-sectional diameter approximates the diameter of the tendon so as to allow measurement of the tendon. The device includes a measurement gradient in operable configuration with the adjustable tube so as to allow measurement of the tendon in the closed configuration. Other embodiments are also disclosed.