The present disclosure provides alert implants that comprise a medical device and an implantable reporting processor (IRP), where one example of such a medical device includes a component for a total knee arthroplasty (TKA) such as a tibial extension, a femoral component for hip replacements, a breast implant, a distal rod for arm or leg breakage repair, a scoliosis rod, a dynamic hip screw, a spinal interbody spacer, and tooling and methods that may be used to form the alert implant, and uses of such alert implants in the health maintenance of patients who receive the implant.

A sports training aid comprising a body unit (110) attachable to a person's body or the person's sports implement wherein the body unit (110) is provided with a positioning sensor module; a feedback stimulator; and a processor. The sports training aid is configured to provide instantaneous feedback on motion faults of a studied sports motion, and the body unit (110) is intended to be attached to a person's body (or a person's sports implement) at a representative location, the location being bound to travel a path representative of the studied sports motion, and the positioning sensor module comprises acceleration sensors and gyro sensors, and the processor is configured to determine a still position corresponding to an event wherein the body unit (110) rs determined to be still, and the processor is configured to keep track of the sensor module's movements relative to the still position, and the processor is configured to activate the feedback stimulator in real time, upon detection of a sports motion fault of the studied sports motion of the person as represented by the motion path of the sensor module.

A device for performing individual movement analysis and movement therapy on a patient includes a control and analysis unit configured to control a first intervention device in such a way that a first trajectory of the movement of an extremity of the patient is disrupted by a force exerted by a movement module onto the extremity of the patient. A response to this disruption is measured by changed measured values from at least one force sensor and/or at least one angle sensor, and a new, second trajectory is calculated therefrom. New control parameters for controlling the first intervention device are calculated from a comparison of the second trajectory with the first trajectory and/or a target trajectory or the comparison of the disrupted measured values with the non-disrupted measured values.

A psychological evaluation device that estimates interest of a subject in a content used integrally with a terminal held by the subject. Acceleration data obtained by an acceleration sensor built in the terminal is acquired, and a frequency analysis is performed on the acquired acceleration data to obtain acceleration in the gravity direction of the terminal. By obtaining the acceleration in the gravity direction of the terminal, it is possible to, for example, estimate the subject's interest in the content based on the integral value of each frequency component of the acceleration in the gravity direction obtained by performing the frequency analysis.

An extended reality-based system for dynamic assessment of upper extremity impairments is disclosed. A virtual environment is provided to a user. A target path is received. A sensory stimulus is presented to the user. Three-dimensional position information is determined for one or more body part defining a recorded path. A plurality of motion parameters corresponding to a user response to the stimulus are determined by: comparing the recorded path to the target path, determining a smoothness metric of the recorded path, determining starting coordinates and ending coordinates of the path, determining a first time period from presentation of the sensory stimulus to when motion of the one or more body part begins, and determining a second time period for motion between the starting coordinates and the ending coordinates. At least one index indicative of upper extremity impairment from the plurality of motion parameters.

This disclosure is directed to techniques for recording and recognizing physiological parameter patterns associated with symptoms. A medical device system includes a medical device including an accelerometer configured to collect an accelerometer signal that indicates one or more patient movements that occur during a cough. Additionally, the medical device system includes processing circuitry configured to: determine whether the accelerometer signal satisfies a set of criteria corresponding to a cough pattern comprising a smooth increase from a baseline, then a sharp decrease, a peak within the sharp decrease, then a gradual return to the baseline; and identify a cough based on the determination that the accelerometer signal satisfies the set of criteria.

A walking training system according to an embodiment includes: a treadmill; a foot sole load detection unit configured to detect load received from foot soles of a trainee aboard a belt of the treadmill; a first photographing device configured to photograph the trainee from a lateral side; a skeletal information acquisition unit configured to acquire first skeletal information that is skeletal information on the trainee in a sagittal plane from an image photographed by the first photographing device; and a specification unit configured to specify respective pieces of skeletal information on a right leg and a left leg included in the first skeletal information acquired by the skeletal information acquisition unit, based on the load received from the foot soles of the trainee detected by the foot sole load detection unit.

The device comprises at least one accelerometer, and a controller receiving input signals from the at least one accelerometer. The controller configured to filter stroke characteristics from the input signal using a filter module. The controller then applies a first statistical module on the filtered signal and obtains a first output signal. Due to the first statistical module, the first output signal is obtained, which is agnostic to type of swim stroke employed by the swimmer. The controller then determines the swim metric based on the first output signal and an adaptive threshold value. The swim metric is lap completion or lap count or turn event, during swimming by a swimmer. The device consumes less power and also agnostic to swim styles and turn styles employed by swimmers.

The abnormal data processing system is provided with: a storage unit for holding a multiple-subject DB in which data on multiple subjects are accumulated and individual-subject DB in which data on individual subjects are accumulated; an individual-subject DB divergence-degree calculation unit for calculating an individual-subject DB divergence degree which is the degree of divergence of the new data from the individual-subject DB; a multiple-subject DB divergence degree calculation unit for calculating a multiple-subject DB divergence degree which is the degree of divergence of the new data from the multiple-subject DB; and a composite divergence degree calculation unit for determining a composite divergence by compositing the individual-subject DB divergence degree and the multiple-subject DB divergence degree using the number of data instances in the individual-subject DB. The abnormal data processing system determines whether or not the new data is abnormal on the basis of the composite divergence degree.

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.