A user interface device is adapted to provide tremor cancellation. The user interface device comprises a user interface for determining a position from a physical user input and a position output for providing a time-ordered output stream of position data, but also provides a tremor learning module and a tremor cancellation module. The tremor learning module can be trained to identify tremor patterns for a user by comparing time-ordered output streams of position data produced by the user with predetermined representations. The tremor cancellation module is adapted to apply the tremor patterns learned for the user to cancel tremors in a time-ordered stream of position data produced by the user to create an output stream of position data which is corrected for user tremor. A method of training and then using such a user interface device is also described.

The invention relates to a device, a method, a computer program product and an application for establishing a current load level of a user. The device and the method comprise a mobile terminal, which comprises at least one sensor generating signal data and a plurality of available applications for use by the user and also an evaluation unit. According to the invention, provision is made for the mobile terminal to comprise an application, which is configured as further application and establishes a plurality of biometric data in respect of the user, at least from the signal data or from available applications used by the user, and to make said data available to the evaluation unit, and for the evaluation unit to determine the current load level of the user from the biometric data.

An information processing apparatus according to the present invention includes: a calculating unit configured to calculate, based on subject information including a first assessment value representing an assessment of a subject at a predetermined moment for each of a plurality of items set in FIM (Functional Independence Measure), a prediction value representing an assessment of the subject predicted after the predetermined moment; and a control unit configured to set the prediction value as a provisional assessment value representing a provisional assessment of the subject for a predetermined item of the FIM, and control to output so as to display in a correctable manner on an information processing device operated by an assessor.

Method and system of training a machine learning neural network (MLNN) monitoring anatomical dynamics of a subject in motion. The method comprises receiving, in a first input layer of the MLNN, from a millimeter wave (mmWave) radar sensing device, mmWave radar point cloud data representing a first gait characteristic; receiving, in a second layer of the MLNN, from the mmWave radar sensing device, mmWave radar point cloud data representing a second gait characteristic; the first and the at least a second input layers being interconnected with an output layer via an intermediate layer having an initial matrix of weights; training a MLNN classifier based on a supervised classification establishing correlation between a degenerative condition of the subject at the output layer and the point cloud data; and adjusting the initial matrix of weights by backpropagation to increase correlation between the degenerative condition and the sets of point cloud data.

A strain sensor comprising a conductive member having a plurality of elements arranged adjacent to one another, and a non-conductive and elastically deformable material encapsulating the conductive member, wherein, in an equilibrium state, compressive forces cause at least one of the plurality of elements to contact at least a portion of an adjacent element, and wherein, when a strain is applied, a resulting elastic deformation causes at least one of the plurality of elements to space apart from an adjacent element such that the contacted portion decreases or is eliminated. A multi-axis force sensor comprising a sensing array comprising at least two planar sensors arranged radially on a planar substrate in antagonistic pairs, and a compressible member positioned between the substrate and a central portion of the sensing array, the compressible member acting to displace the central portion of the sensing array away from the substrate.

An athletic performance estimation apparatus evaluates athletic performance from the movement of eyes when observing the movement of a target. A control circuitry (11) selects a measurement condition. A video presentation circuitry (12) presents video according to the measurement condition to an observed person. An eyeball movement measurement circuitry (13) measures the movement of eyes of the observed person who is observing the movement of a target. An analysis circuitry (14) obtains a feature value based on an eyeball movement, from time series information on the movement of the eyes. A normalization circuitry (15) integrates the obtained feature value with a feature value obtained under a different measurement condition and performs normalization. An estimation circuitry (16) estimates the athletic performance of the observed person from a normalized feature value.

A rehabilitation work support apparatus, a rehabilitation work support method, and a program capable of efficiently managing patient's actual current ability values are provided. A rehabilitation work support apparatus (1) includes a change prediction unit (2) that predicts whether or not an actual current ability value of a patient during a rehabilitation period has changed from a latest ability value of that patient stored in a storage device based on an elapsed time from a time of an evaluation of the latest ability value of the patient stored in the storage device, and a change prediction output unit (3) that performs control so as to output a result of the prediction made by the change prediction unit (2).

The invention provides a method of treating, preventing or ameliorating Parkinson's disease with Prasinezumab.

In a method of neurological assessment, multichannel electromyography (EMG) data are acquired for an anatomical region. A pairwise EMG channel-EMG channel similarity matrix is generated from the acquired multichannel EMG data. Network analysis is performed on the similarity matrix to generate a network representing the similarity matrix. One or more metrics of the network are computed. One or more biomarkers are determined for the anatomical region based on the one or more metrics. In another method, EMG data are acquired using an electrode array contacting skin of a target anatomy, the EMG data are processed to produce reduced-dimensionality data; and time-invariant muscle synergies and corresponding time-varying activation functions are determined in the reduced-dimensionality data.

A system for monitoring exercise activity in a gym environment includes an image-based monitoring arrangement, a storage device, and a replay device. The monitoring arrangement generates a time series of 3D representations of an individual, with each 3D representation defining a pose of the individual and comprising 3D positions of a plurality of predefined feature points of the individual. The storage device receives and stores an exercise sequence, which comprises at least part of the time series of 3D representations and corresponds to an exercise performed by the individual. The replay device retrieves the exercise sequence from the storage device, renders an animation of the individual when performing the exercise based on the exercise sequence, and provides the animation for display. The system enables a user to review the exercise at any desirable viewing angle and/or magnification.