A joint replacement balancing system which provides real-time feedback to a surgeon during a joint replacement surgery to assist the surgeon to balance a joint replacement. The joint replacement balancing system includes a non-transitory processor-readable medium storing code representing instructions to cause a processor to receive a signal from a joint balancing apparatus, determine if the joint replacement is out of balance, determine a corrective course of action to bring the joint into balance and generate and display to the surgeon during the joint replacement surgery a recommended corrective course of action to complete the joint replacement surgery.

A method for calibrating respective estimated joint axis directions for each of a pair of body mounted sensors, one of the pair of sensors being located to each side of the joint comprising a joint axis, the sensors each calculating a pitch angle about respective first sensor axes and a roll angle about respective second sensor axes, the first and second sensor axes together with a third sensor axis orthogonal to the first and second sensor axes forming a sensor frame, the method comprising: receiving orientation data for each of the two sensors, the orientation data being associated with at least two different poses of the joint for each of the two sensors and the orientation data comprising the pitch angle and the roll angle of the sensor for each pose; calculating a sensor frame estimated gravity vector for each pose associated with each sensor based on the pitch and roll angles for each pose associated with each sensor and a gravity vector running along a vertical direction; and determining the estimated joint axis directions for the joint axis, relative to the first and second sensor axes, for each sensor that minimise a loss function concerning projections of each sensor frame estimated gravity vector for each pose associated with each sensor on to the estimated joint axis direction for the respective sensor.

Computing devices, wearable devices, networked environments, and methods for bio-signal acquisition and monitoring skeletomuscular parameters are provided. First data indicative of movement parameters of knee joint of a patient for a test type is obtained from a wearable device. Second data indicative patient-specific parameters of the patient are obtained. A cluster to which the patient belongs is identified based on the patient-specific parameters and a prediction model. A normative range for the test type for the cluster is determined. The first data is processed to determine whether the patient falls within the normative range of the cluster and result is provided.

Prostheses are described for stabilizing dysfunctional sacroiliac (SI) joints. The prostheses are sized and configured to be press-fit into surgically created pilot SI joint openings in dysfunctional SI joint structures. The prostheses have a pontoon shape with opposed elongated partially cylindrical sections connected by a bridge section. The partially cylindrical sections and, in some instances, the bridge section have a porous structure.

An orthopedic system to monitor a parameter related to the muscular-skeletal system is disclosed. The orthopedic system includes electronic circuitry, at least one sensor, and a computer to receive measurement data in real-time. The orthopedic system comprises a first plurality of shims of a first type, a second plurality of a second type, a measurement module, and the computer. The measurement module houses the electronic circuitry and at least one sensor. The measurement module is adapted to be used with the first plurality of shims and the second plurality of shims. The measurement module has a medial surface that differs from a lateral surface by shape, size, or contour.

Systems and methods for monitoring the status of a joint or limb using color sensing. First and second color sensors are provided to sense first and second color encoded surfaces at first and second locations of a joint or limb, respectively. The color sensing data are processed to obtain the respective motion information of the first and second locations.

Disclosed is a rehabilitation training apparatus. The rehabilitation training apparatus includes a pair of first tracks formed parallel to each other along first axis, a second track disposed on the pair of first tracks, and moving along the first axis, and formed along second axis, a hand holder disposed on the second track, and moving along the second track, and on which at least one of hand and arm of a user is held, a stopper for a track disposed on the pair of first tracks for limiting a movement range of the second track, and a stopper for a holder disposed on the second track for limiting a movement range of the second pair of first tracks, wherein the first axis and the second axis are disposed with an inclination.

System, apparatus, and method for automatic detection of arthritis according to temperature asymmetry estimation in contralateral joints is presented. Simultaneously recorded thermogram and the optical image of an inspected joint and its contralateral joint are sent to the processing unit, where they are stored, processed, and analyzed. The system, apparatus, and method automatically detects outlines of joints in thermograms and optical images. Grid of points of interest is distributed inside the inspected and the contralateral joint's outline. Temperature maps are calculated according to both grids points and the temperature disparity map is estimated. The set of inflammation regions is obtained by analyzing the temperature disparity map and collecting adjacent points containing temperature differences surpassing the threshold. The system, apparatus, and method are non-invasive and non-contact, and suitable for real world environments with natural home or health care institutions background.

A method can include receiving (1) images of at least one subject and (2) at least one total mass value for the at least one subject. The method can further include executing a first machine learning model to identify joints of the at least one subject. The method can further include executing a second machine learning model to determine limbs of the at least one subject based on the joints and the images. The method can further include generating three-dimensional (3D) representations of a skeleton based on the joints and the limbs. The method can further include determining a torque value for each limb, based on at least one of a mass value and a linear acceleration value, or a torque inertia and an angular acceleration value. The method can further include generating a risk assessment report based on at least one torque value being above a predetermined threshold.

A system and device (110) for determining bone laxity. For example, the system includes a tracked probe (300) comprising at least one probe marker (310) and a computer assisted surgical (CAS) system (100). The CAS system includes a navigation system (130) and a processing device (110) operably connected to the navigation system and a computer readable medium configured to store one or more instructions that, when executed, cause the processing device to receive location information from the navigation system, generate (820) a surgical plan comprising a post-operative laxity assumption (720), collect (850) first motion information related to movement of the joint through a first range of motion, collect (860) second motion information related to movement of the joint through a second range of motion, determine (870) a post-operative laxity (710), and compare the post-operative laxity and the post-operative laxity assumption to determine laxity results.