A novel multiple camera calibration algorithm uses human joint points for matched key points. A recent machine-learning based human joint detector provides joint positions with labels (e.g. left wrist, right knee, and others). In single person situation, it directly provides matched key points between multiple cameras. Thus, the algorithm does not suffer a key-point matching problem, even in a very sparse camera configuration, which is challenging in the traditional image feature-based method. This algorithm provides easy setup for a multiple camera configuration for marker-less pose estimation.

A system and method for determining patient-specific anatomical parameters to improve surgical outcomes. Some embodiments include processes for predicting the parameters of occluded anatomy. Some embodiment includes processes for more accurately identifying a center point of a ball and socket joint, such as a center point or center of rotation of a femoral head. Some embodiments include processes for identify a patient-specific spinal curvature, including more precisely determining patient specific spinal inflection points. The various steps can be performed automatically through trained computing devices and graphically presented to a surgeon for review and any necessary modifications.

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.

The invention relates to a wearable device for determining a nature of motion and/or a physiological state of a wearer. The wearable device comprises a body portion configured to be worn by the wearer and at least one sensor mounted to the body portion configured to, while the wearable device is being worn, detect a motion parameter of the wearer and/or a physiological parameter of the wearer and generate input data indicative of the motion parameter and/or the physiological parameter. The wearable device further comprises a processor configured to receive the input data from the at least one sensor and process the input data by executing an algorithm to determine the nature of motion and/or the physiological state of the wearer while the wearable device is being worn. The invention further relates to a system for determining a nature of motion and/or a physiological state of a wearer.

Knee joint laxity testing apparatus configured to measure knee laxity in three planes of motion includes a foot and ankle stabilization assembly including a foot plate, a heel clamp and a tibial clamp for securing a foot and an ankle of a person, an anterior-posterior (AP) loading assembly configured to apply an anterior/posterior loading on a knee of the person, and a thigh stabilization assembly including a proximal thigh fixation module and a distal thigh fixation module. The proximal thigh fixation module includes a thigh cradle and a pair of clamping arms for securing a thigh positioned on the thigh cradle. Each clamping arm is pivotable about a hinge. The distal thigh fixation module is operable to firmly hold a distal portion of a thigh of the person in place while a leg of the person is manipulated.

A system is disclosed herein for providing a kinetic assessment and preparation of a prosthetic joint comprising one or more prosthetic components. The system comprises a prosthetic component including sensors and circuitry configured to measure load, position of load on a curved surface, joint stability, range of motion, and impingement. In one embodiment, the system is for a ball and socket joint of a musculoskeletal system. The system further includes a computer having a display configured to graphical display quantitative measurement data to support rapid assimilation of the information. The kinetic assessment measures joint alignment under loading that will be similar to that of a final joint installation. The kinetic assessment can use trial or permanent prosthetic components. Furthermore, adjustments can be made to the applied load magnitude, position of load, and joint alignment by various means to fine-tune an installation.

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.

A detecting method and a positioning analysis method of human functional joint rotation center are provided. The detecting method of human functional joint rotation center includes: step 11: in a continuous motion, a human functional joint rotation center FCR is abstracted as a center of a flexible ball; step 12: at any moment during a test, position coordinates of the center of the ball (i.e. FCR) at the moment are determined according to position coordinates of M1, M2 and M3, and then the motion trajectory of the FCR is obtained in the continuous motion; the positioning analysis method performs positioning analysis of joint positions based on morphological parameters collected by 3D scanning. The detecting method is based on an idea of flexible ball, its operation is simple within a certain error range, and the method performs very well in the continuity of trajectory of joint.

A sleep stage estimation device includes a subject data acquisition unit that acquires pulsation data and body movement data of a subject, a sleep stage probability estimation unit that acquires a feature quantity sequence from the pulsation data and estimates a sleep stage probability sequence of the subject from the acquired feature quantity sequence by using a learned sleep stage probability estimation model, a sleep stage transition probability estimation unit that acquires a body movement amount sequence from the body movement data and estimates a sleep stage transition probability sequence of the subject from the acquired body movement amount sequence by using a learned sleep stage transition probability estimation model, and a sleep stage estimation unit that estimates a sleep stage sequence of the subject from the sleep stage probability sequence and the sleep stage transition probability sequence by using a learned conditional random field model.

A walking training system includes a treadmill, a center-of-gravity position detection unit that detects a center-of-gravity position of a trainee from a load received from a sole of the trainee on a belt of the treadmill, a posture detection unit that detects a posture of the trainee, a center-of-gravity position estimation unit that estimates the center-of-gravity position of the trainee from the detected posture of the trainee, a determination unit that determines whether a difference between the detected center-of-gravity position and the estimated center-of-gravity position exceeds a predetermined value, and a notification unit that sends, when the determination unit determines that the difference exceeds the predetermined value, a notification notifying that whether the difference exceeds the predetermined value.