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 solution for non-invasive determination of supraphysiological body joint kinematics. The solution obtains external images related to a test procedure of the body joint and performs image analysis on the obtained images to define a pattern of a plurality of spatial points in a region of interest. Each individual spatial point is defined by a unique pattern of neighboring surrounding pixels in each image, and the pattern is part of a high-contrast speckle pattern applied to the body joint. The solution identifies displacements of the spatial points in subsequently obtained images by tracing a location of the unique pattern of neighboring pixels in each image in relation to a base image of the body joint, calculates deformation measures from the displacements of the plurality of spatial points, and obtains deformation measures of a reference body joint. The solution compares the deformation measures and determines supraphysiological body joint kinematics from the comparison.

A method for facilitating a Parkinson's Disease (“PD”) assessment of a patient includes capturing first video of a patient performing first test movements while holding the mobile device; capturing second video of the patient performing second test movements while maintaining the mobile device on their person; capturing third video of the patient performing third test movements including standing and walking; capturing one or more IMU readings using an IMU of the mobile device; processing the first video, the second video, and the third video according to (i) a hand landmark model to generate one or more hand biomarkers, (ii) a face landmark model to generate one or more face biomarkers, and (iii) a body landmark model to generate one or more body biomarkers; and determining an assessment score based on a standardized PD assessment by processing the biomarkers.

An example apparatus includes a shell portion configured to be worn over a head of a subject. The shell portion defines a plurality of apertures. The apparatus also includes a plurality of spherical fiducial structures disposed on the shell portion. Each of the fiducial structures includes a first material doped with a second material. The second material is a contrast agent for magnetic resource imaging (MRI). The apparatus also includes a mounting structure disposed on the shell portion and configured to secure the shell portion to the head of the subject.

A processing device receives, from a three-dimensional (3D) motion capture system, initial data representing an initial orientation of a subject user's body in an initial position. The processing device further receives x-ray data representing at least the portion of the subject user's body in the initial position. The processing device determines an actual orientation of at least one bone or joint from the portion of the subject user's body in the initial position as represented in the x-ray data and calibrates the initial orientation of the 3D motion capture system to reflect the actual orientation of the at least one bone or joint in the initial position.

Method for detecting biomechanical and functional parameters of the knee in a situation of performance stress, which comprises: a step for the set up of video recording means (1); a step for the set up of multiple optical markers (4) at specific landmark anatomic points (PA) of the foot and of the knee of a person; a step for the set up, at the plantar surface of the foot, of baropodometric means (5); a step for the acquisition, by means of the video recording means (1), of images of at least one reference action, and a step for the detection, by means of the baropodometric means (5), of baropodometric parameters; a step for calculating, from the baropodometric parameters, the coordinates of the center of pressure (COP) of the foot and of the constraining reaction force (FV) acting on the foot; a step for calculating a force arm (BF) given by the distance between the center of pressure (COP) and a reference point (PR) of the knee; a step for calculating a biomechanical parameter indicative of the valgus moment, derived from the vector product (PV) of the force arm (BF) and the constraining reaction force (FV).

Arrangements described herein relate to a headset system and a method to manufacturing the headset system, the headset system including a headset configured to lay on top of a surface and to support a head of a subject when the subject is in a supine position or a reclined position, at least one probe adjustment mechanism, and a probe coupled to the at least one probe adjustment mechanism and configured to emit acoustic energy.

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 method for obtaining a spatial pattern of an anatomical structure of a subject includes comprising the steps of a) acquiring, from at least one digital image capturing device, at least one uncalibrated image of a calibration reference applied on a surface configured to receive the subject, the calibration reference having at least one known dimension and defining at least one known direction; b) defining an absolute calibrated reference system of three coordinates based on the calibration reference depicted in the at least one uncalibrated image; and c) acquiring, from the at least one digital image capturing device, at least a first and at least a second calibrated image of a plurality of markers applied on a corresponding plurality of body landmarks of the anatomical structure of the subject at respective contact points with the body landmarks, the plurality of markers being arranged within the absolute calibrated reference system,

An information processing method that is executed by a computer, the method comprising: acquiring motion information detected by a motion detection device, the motion detection device configured to detect a motion of a subject; storing the acquired motion information; deriving reference motion information on a left half of a body of the subject and reference motion information on a right half of the body of the subject based on the stored motion information in a predetermined period; and determining whether an abnormal state in which there is a possibility of cerebral infarction in the subject is present based on the derived reference motion information and motion information on the left half of the body of the subject and motion information on the right half of the body of the subject at a detection time subsequent to the predetermined period.