A method and system of training a machine learning neural network (MLNN) in anatomical degenerative conditions in accordance with anatomical dynamics. The method comprises receiving, in a first input layer of the MLNN, from a millimeter wave (mmWave) radar sensing device, a first set of mmWave radar point cloud data representing a first gait characteristic of a subject in motion, comprising an arm swing velocity, receiving, in a second layer, a second set of mmWave radar point cloud data representing a second gait characteristic comprising a measure of dynamic postural stability, the input layers being interconnected with an output layer of the MLNN via an intermediate layer, and training a MLNN classifier in accordance with a classification that increases a correlation between a degenerative condition of the subject as generated at the output layer and the sets of mmWave point cloud data.

Conventionally, a neuronal controller located inside the central nervous system governing the maintenance of the upright posture of the human body is designed from a control system perspective using proportional-integral-derivative (PID) controllers, wherein human postural sway is modeled either along a sagittal plan or along a frontal plane separately resulting in limited insights on intricacies of a governing neuronal controller. Also, existing neuronal controllers using a reinforcement learning (RL) paradigm are based on complex actor-critic on-policy algorithms. Analyzing human postural sway is critical to detect markers for progression of balance impairments. The present disclosure facilitates modelling the neuronal controller using a simplified RL algorithm, capable of producing postural sway characteristics in both sagittal and frontal plane together. The Q-learning technique of the RL paradigm is employed for learning an optimal state-action value (Q-value) function for a tuneable Markov Decision Process (MDP) model.

A force measurement system is disclosed herein. The force measurement system includes a force measurement assembly configured to receive a subject, and one or more data processing devices operatively coupled to the force measurement assembly. In one embodiment, the one or more data processing devices are configured to determine a difference between a center of pressure and a center of mass for the subject from output force and/or moment data, and to determine the fall risk of the subject based upon the difference between the center of pressure and the center of mass for the subject. In another embodiment, the one or more data processing devices are configured to determine one or more balance parameters of the subject from the output force and/or moment data while the subject is stepping onto the top surface of the force measurement assembly and/or stepping off the top surface of the force measurement assembly.

Systems and methods are disclosed for human balance training. The systems and methods involve intermittently occluding the vision of a subject while the subject is engaged in a balance activity, such as walking on a balance beam, for example. Creating a brief period of continuous visual occlusion results in posterior parietal cortex activation, leading to enhanced balance training. The end result is much greater improvement in balance compared to physical training alone.

The head-position sway measuring device (D) includes a touch panel (1a) that gives the instruction to open or close eyes, an acceleration sensor (2a) that measures a displacement of a head position, and a sway recognition unit (1b) that recognizes a head-position sway value based on the displacement of the head position. The acceleration sensor (2a) measures a first measurement value, which is a measurement value obtained in the eye-open state, and then measures a second measurement value, which is a measurement value obtained in the eye-closed state. The sway recognition unit (1b) recognizes the head-position sway value based on the first measurement value and the second measurement value.

In order to help reduce the effects of motion sickness, there is provided a method for reducing motion sickness in a subject which comprises acquiring a sequence of video images, extracting measurements of a heart-rate of the subject over a first period of time from the sequence of video images using photoplethysmography (PPG), calculating at least one trend in the measurements, determining a presence of motion sickness when the at least one trend is positive over a first time window, the first time window being included in the first period of time, and generating an event arranged to generate a corrective action. It is often possible to detect the onset of motion sickness before the subject actually feels the symptoms. Indeed, by the time the symptoms appear, corrective action is much less effective. Therefore, by detecting the onset early and alerting the subject so that they can react, it is possible to avoid the attack of motion sickness or, at least, reduce significantly its effects.

Various embodiments provide systems and methods for identifying impairment using measurement devices.

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.

A system for screening impairment of a subject includes an imaging device and a display connected to a controller, the controller configured to send a first eye test signal to the imaging device and a second eye test signal to the display to initiate an eye test, and further configured to receive an eye test feedback signal based on captured images of subject eye movement during the eye test. The controller is configured to send a first cognitive test signal to the imaging device and a second cognitive test signal to the display to initiate a cognitive test, and is further configured to receive a cognitive test feedback signal based on captured images of subject eye movement during the cognitive test. A balance sensor is connected to the controller, the controller configured to receive a balance test feedback signal from the balance sensor indicative of subject movement during a balance test. A physiological sensor is connected to the controller, the controller configured to receive a physiological activity feedback signal from the physiological sensor indicative of subject physiological activity. The controller is configured to generate an impairment indication based on the eye test feedback signal, the cognitive test feedback signal, the balance test feedback signal, and the physiological activity feedback signal. A method for screening impairment of a subject is also disclosed.

This disclosure relates generally to a Parkinson's disease detection system. Parkinson's disease is a neuro-degenerative disorder affecting motor and cognitive functions of subjects. Since symptom manifestation is limited in Parkinson's disease, identifying Parkinson's disease in the early stage is a challenging task. The present disclosure overcomes the limitations of the conventional methods for detecting Parkinson's disease by utilizing a graph theory approach. Here, each pressure sensor attached to an insole corresponding to a plurality of pressure points associated with a foot of the subject is considered as a node of a connectivity graph. The foot dynamics analysis is performed based on a metric known as mediolateral stability index and the mediolateral stability index is calculated by utilizing a betweenness centrality associated with each node of the connectivity graph. Further, the mediolateral stability index is compared with standard values to detect the intensity of the Parkinson's disease.