Disclosed is a wearable physical therapy data collection system. The system includes a wearable support having a waistband and at least one motion detector assembly. The motion detector assembly includes a femoral strut extending between a superior end and an inferior end; a polyaxial connection between the superior end and the waistband; and hip sensors in motion sensing communication with the polyaxial connection, configured to capture data reflecting movement of the femur with respect to the hip in the medial—lateral direction, anterior—posterior direction and rotation of the femur. A tibial strut may extend between the inferior end and a polyaxial connection to a foot attachment. Sensors may be provided to capture data reflecting tibial rotation, and flexion, extension, rotation and pronation of the foot. Separate left and right motion detectors enable collection of bilateral data, to identify bilateral imbalances and monitor rehabilitation progress.

Laser-based implant guide systems and methods that align an implant with an axis of an anatomical structure of interest are disclosed. The systems include a target base configured to couple to a patient in alignment with the axis, and a target member configured to couple to the target base that includes a visual indication of the location of the axis. The systems further include an implant guide that includes a laser device and a resection guide. The implant guide is configured to adjust at least one of the position and the orientation of the laser device with respect to the anatomical structure of interest such that a laser line projecting from the laser device is aligned with the visual indication of the target member, and the resection guide facilities implantation of the implant in a resected portion of the anatomical structure of interest in alignment with the axis.

Systems and methods relating to a smart elastic fabric tape are disclosed. For example, a method may include interrogating a sensing mesh using an electrical impedance tomography (EIT) device, wherein the sensing mesh is affixed onto skin nearby a musculoskeletal (MSK) region of interest, wherein the sensing mesh comprises a nanocomposite thin film disposed on elastic fabric tape, and wherein the sensing mesh forms a geometrical pattern on the skin; generating, in real-time, EIT conductivity maps from interrogating the sensing mesh; and generating, in real time, strain distribution and strain directionality data of the MSK region of interest based on the EIT conductivity maps.

A surgical guide includes a body having four sides. The body comprises a first base at the first side, a second base at the second side and an elongated extension between the first and second base. The first base defines at least one first hole and the second base defines at least a second hole. The body is sized and configured to be installed above a joint line of a calcaneus and a talus bone and approximately mid-way along a longitudinal axis of a tibia. The first base is configured to receive at least one dowel of a second surgical guide in the at least one first hole and the second base is configured to receive at least one pin sleeve and pin in the second hole. Systems and methods are also disclosed.

Alignment instruments may include joint-line referencing systems having an alignment arm having a body with first and second portions defining first and second sides. The first portion has at least one first pin tube through-hole extending from the first to the second side. The second portion has a first opening on the first side. A pin tube guide member is receivable in the first through-hole. The pin tube guide member has a passageway therethrough. An angelwing alignment member includes a portion receivable in the first opening of the alignment arm. An alignment foot is secured to the second portion, and the alignment foot has a handle and a shim. The shim is positionable in a joint between a first bone and a second bone, the alignment arm is alignable relative to a first bone, and the pin tube guide is operable for securing a pin into the first bone.

Disclosed herein are various patient measurement devices having an extendable elongate body and a deployable arm rotatably coupled to the elongate body. The elongate body has a first body comprising a lumen defined along a length of the first body and a second body extendably disposed within the lumen of the first body. The deployable arm is rotatably coupled to an end of the second body and is rotatably movable between a retracted position and a deployed position. Also disclosed are various methods of using the device to take various range-of-motion measurements, other physical capability measurements, and anthropometric measurements.

An exoskeleton device is provided herein that includes a control unit including a controller. At least one embedded sensor is configured to acquire data. An actuator is in electrical communication with the at least one embedded sensor and the controller. The controller is configured to adjust a level of assistance or resistance provided by the actuator in response to a change in a performance metric as measured by the acquired data.

A method and system is provided for finding and analyzing gait parameters and postural balance of a person using a Kinect system. The system is easy to use and can be installed at home as well as in clinic. The system includes a Kinect sensor, a software development kit (SDK) and a processor. The temporal skeleton information obtained from the Kinect sensor to evaluate gait parameters includes stride length, stride time, stance time and swing time. Eigenvector based curvature detection is used to analyze the gait pattern with different speeds. In another embodiment, Eigenvector based curvature detection is employed to detect static single limb stance (SLS) duration along with gait variables for evaluating body balance.

A presymptomatic disease countermeasure system 90 includes a motion measurement unit 91 which obtains motion data that is time-series data representing motion, by measuring the motion of an object, a joint reaction force computation unit 92 which computes joint reaction force at a joint to be evaluated among joints of the object, using the obtained motion data and ground reaction force data that is time-series data representing ground reaction force applied to the object, a feature amount computation unit 93 which computes a feature amount representing a load repeatedly applied to the joint to be evaluated on the basis of the computed joint reaction force, and a determination unit 94 which determines a joint disorder risk indicator that is an indicator representing a joint disorder risk that is the risk of causing joint disorder, on the basis of the computed feature amount.

A method of using an ankle exoskeleton device is provided herein. The method includes collecting one or more biomechanical data points from an individual. The method also includes developing individualized musculoskeletal simulations based on the one or more biomechanical data points. In addition, the method includes creating predictive simulations by modeling effects of an ankle exoskeleton device on the individualized musculoskeletal simulations. The method also includes utilizing established device-user relationships with real-time measurements to adjust device control. Lastly, the method includes optimizing design and control parameters of the exoskeleton device based on the predictive simulations and user responses.