A method and a related device to determine the kinetic state of a subject includes the steps of determining a signal indicative of the acceleration trend on the three Cartesian axes; processing the signal to limit the frequency band and preferably reduce artifacts and compensate the offset of the output signals from a multi-axial measurement system; analyzing frequency and spectrum through the transformation of the signal with the Fournier transform; computing the power spectral density for each Cartesian axis; and comparing the spectral density with a characteristic pattern of a movement.

In an example method, a mobile device obtains sample data generated by one or more sensors over a period of time, where the one or more sensors are worn by a user. The mobile device determines that the user has fallen based on the sample data, and determines, based on the sample data, a severity of an injury suffered by the user. The mobile device generates one or more notifications based on the determination that the user has fallen and the determined severity of the injury.

An apparatus for making combined vestibular and somatosensory function assessments, comprising: a portable base unit comprising: a movable platform being at least partially rotatable about an axis of rotation; in use, a user stands with both feet on the movable platform; an adjustable stopping mechanism for adjusting an extent to which the movable platform can rotate in at least one direction with respect to a horizontal plane; and a controller for controlling the adjustable stopping mechanism to selectively adjust the extent to which the movable platform is rotated by one of a plurality of discrete measurable amounts based on a control signal; a visual occlusion headset worn by the user, the headset comprising a device for recording a vestibular function response in response to a vestibular function test.

Systems and methods may be used for evaluating a patient after completion of an orthopedic surgery on a portion of a body part of the patient. In an example, the method includes capturing, using a camera of the device, a series of images of the patient in motion, determining respective lengths of the body part in each of the series of images based on comparing the body part in each of the series of images to a skeletal model, and identifying a maximum length of the body part from the respective lengths. The method may include displaying an indication corresponding to the maximum length.

Methods, apparatus and systems for radio-based sleep tracking are described. In one example, a described system comprises: a transmitter configured to transmit a first wireless signal through a wireless multipath channel in a venue; a receiver configured to receive a second wireless signal through the wireless multipath channel, wherein the second wireless signal differs from the first wireless signal due to the wireless multipath channel which is impacted by a sleeping motion of an object in the venue; and a processor. The processor is configured for: obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the second wireless signal, wherein each channel information (CI) of the TSCI comprises N1 components, wherein N1 is a positive integer larger than one, computing N1 component-wise analytics each associated with one of the N1 components of the TSCI, identifying N2 largest component-wise analytics among the N1 component-wise analytics, wherein N2 is a positive integer less than N1 computing at least one first motion statistics based on the N2 largest component-wise analytics of the TSCI, and monitoring the sleeping motion of the object based on the at least one first motion statistics.

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 method and a wearable device are provided. The method includes providing a swimming mode in the wearable device, and storing standard swimming stroke data that have been collected in advance as corresponding template data, when a monitoring process starts, activating the swimming mode according to an instruction given by a user who will immediately enter water, and after the swimming mode has been activated, controlling a sensor to collect swimming stroke data of the user; obtaining test data for identifying a swimming state of the user from the swimming stroke data; and matching the test data with each template data, when the test data successfully matches the template data, identifying the swimming state of the user to be the swimming state that corresponds to the template data.

Systems, devices, and methods are disclosed for the purpose of quantitatively determining the susceptibility of a human subject to injure their Anterior Cruciate Ligament (ACL). A method for determining injury susceptibility scores or risk categories includes determining hip extension angles and knee varus angles during the performance of a stork test and determining hip abduction angles during a squat test. Determination of angles during certain movements may be achieved using various systems and devices, including wearable devices.

A system for estimating a three dimensional pose of one or more persons in a scene is disclosed herein. The system includes one or more cameras and a data processor configured to execute computer executable instructions. The computer executable instructions include: (i) receiving one or more images of the scene from the one or more cameras; (ii) extracting features from the one or more images of the scene for providing inputs to a first branch pose estimation neural network and second branch pose estimation neural network; (iii) generating a first training signal from the second branch pose estimation neural network using a three dimensional reconstruction module for input into the first branch pose estimation neural network; (iv) generating one or more volumetric heatmaps; and (v) applying a maximization function to the one or more volumetric heatmaps to obtain a 3D pose of one or more persons in the scene.

A positioning method of functional rotation center of shoulder based on rigid upper arm model includes: step 1: abstracting a human upper arm into a cylinder with FRCS as a center of top surface; step 2: determining a reference axis vector of the cylinder; step 3: determining an axis vector of the cylinder and a displacement from the reference axis vector to the axis vector; step 4: correcting a central axis direction of the cylinder; step 5: determining a height compensation of the cylinder, and positioning the FRCS. The method has higher accuracy for the positioning result of FRCS, the positioning result of FRCS has better stability relative to the upper arm and trunk, and can be used to establish a more accurate human digital dynamic model and predict more accurate human posture.