Systems and methods may be used to perform robot-aided surgery. A system may include a display device and a computing device including a memory device with instructions. The instructions can cause the system to access surgical data, calculate medial and lateral gap data, calculate a recommended component set, and generate a graphical user interface. Accessing surgical data can include accessing soft tissue data indicative of at least tension in soft tissues surrounding a surgical location. The graphical user interface can include an interactive trapezoidal graphic overlaid onto a graphical representation of a distal femur and a proximal tibia. The interactive trapezoidal graphic can include a graphical representation of a medial total gap, a lateral total gap, and a recommended spacer size. The interactive trapezoidal graphic can update in response to adjustments in implant parameters to assist in surgical planning.

An augmented neuromuscular training system and method for providing feedback to a user in order to reduce movement deficits associated with injury risk, prior injury or disease pathology.

A method of manufacturing an arthroplasty jig is disclosed herein. The method may include the following: generate a bone model, wherein the bone model includes a three dimensional computer model of at least a portion of a joint surface of a bone of a patient joint to undergo an arthroplasty procedure; generate an implant model, wherein the implant model includes a three dimensional computer model of at least a portion of a joint surface of an arthroplasty implant to be used in the arthroplasty procedure; assess a characteristic associated with the patient joint; generate a modified joint surface of the implant model by modifying at least a portion of a joint surface of the implant model according to the characteristic; and shape match the modified joint surface of the implant model and a corresponding joint surface of the bone model.

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.

A forearm assessment and training device has a main support, a plurality of finger motion transmission members, a plurality of finger receivers, and a control module. Each of the finger motion transmission members has a member body with a first end and a second end. The first end of the member body of each of the finger motion transmission members is connected to the main support. Each of the finger receivers is connected to the member body of one of the finger motion transmission members. Each of the finger receivers has a finger aperture. The control module is connected to the main support. The control module includes a control module processor, a control module memory, and a sensor. The sensor is configured to measure a force applied to at least one of the finger motion transmission members.

A method for diagnosing a joint condition includes in one embodiment: creating a 3d model of the patient specific bone; registering the patient's bone with the bone model; tracking the motion of the patient specific bone through a range of motion; selecting a database including empirical mathematical descriptions of the motion of a plurality actual bones through ranges of motion; and comparing the motion of the patient specific bone to the database.

A computing system having at least one sensor, at least one processor, and at least one memory including a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the computing system to receive one or more surgical parameters associated with a ligament balancing of a patient's joint, receive real-time sensor data generated by the at least one sensor and indicative of at least one characteristic of the patient's joint, and apply machine learning to determine a next ligament balancing step of the ligament balancing of the patient's joint based on the one or more surgical parameters and the real-time sensor data, wherein the next ligament balancing step is a step of one or more steps intended to result in a target state of the patient's joint identified by the machine learning.

A wireless system comprising a first wireless device and a second wireless device. The first wireless device is configured to operate with less than 15 milliamperes of current. The second wireless device has an internal power source and is configured to transmit one or more radio frequency signals to the first wireless device. The first wireless device is configured to receive the one or more radio frequency signals from the second wireless device. The first wireless device is configured to harvest energy from the one or more radio frequency signals. The first wireless device is enabled for operation after a predetermined amount of energy is harvested from the one or more radio frequency signals. A communication handshake occurs between the first and second wireless devices to indicate that the first wireless device is in communication with the second wireless device. The first wireless device is configured to perform at least one task from harvested energy.

Disclosed herein is a joint evaluation device and corresponding systems and methods for evaluating ligaments and other soft tissue connections between, for example, a tibia and a femur at a knee joint. The joint evaluation device being arranged and configured to induce and measure forces while positions of the tibia and femur are monitored. Some embodiments use a computer assisted surgery device to track positions of the tibia and the femur, and may include a computing device configured to evaluate the forces measured between a first engagement portion and a second engagement portion of the joint evaluation device to output information related to the status of the ligament and other soft tissue connections prior to any bone resections being made to the tibia or the femur.

Systems and methods are disclosed for measuring a femoral alignment of a femur of a patient via a surgically implanted measurement device. An exemplary method may include: receiving, via at least one processor, first data from the measurement device, wherein: a housing of the measurement device is coupled to a musculoskeletal system of a patient; and the first data includes a plurality of measurements from each of an accelerometer and a gyroscope included with an inertial measurement unit disposed within the housing; determining the femoral alignment based on the first data; and causing a display to output the determined femoral alignment.