Users of head-mounted displays (HMDs) often experience nausea and other symptoms often referred to a virtual reality sickness. Various health and biometric sensors are employed to gather baseline sensor measurements. The HMD or a device associated with it can then generate a range or threshold such that any future sensor measurements that fall outside of the range or cross the threshold suggest a health concern for the user of the HMD. The HMD can also be coupled to a microphone, and natural language processing (NLP) is employed to detect negative words or noises made by the user that could also suggest a health concern for the user. After detecting the health concern, the HMD can warn its user, can shut off certain functions, can communicate with an external device, or can trigger an alarm.

Disclosed are an electronic device and operating method thereof. The present invention includes recognizing an electrode initially in contact with the user's body part among a plurality of electrodes as a first pair, recognizing the rest of the electrodes contacted with the user's body as a second pair, measuring a current flowing between the recognized first and second pairs, determining a biometric information of the user based on the measured current and load, and outputting the determined biometric information.

In one embodiment, one or more suspension systems of a vehicle may be used to mitigate motion sickness by limiting motion in one or more frequency ranges. In another embodiment, an active suspension may be integrated with an autonomous vehicle architecture. In yet another embodiment, the active suspension system of a vehicle may be used to induce motion in a vehicle. The vehicle may be used as a testbed for technical investigations and/or as a platform to enhance the enjoyment of video and/or audio by vehicle occupants. In some embodiments, the active suspensions system may be used to perform gestures as a means of communication with persons inside or outside the vehicle. In some embodiments, the active suspensions system may be used to generate haptic warnings to a vehicle operator or other persons in response to certain road situations.

A hybrid MEMS microfluidic gyroscope is disclosed. The hybrid MEMS microfluidic gyroscope may include a micro-machined base enclosure having a top fluid enclosure, a fluid sensing enclosure and a bottom fluid enclosure. The hybrid MEMS microfluidic gyroscope may include a plurality of cantilevers disposed within the bottom semi-circular portion of the micro-machined base enclosure or a single membrane disposed within the bottom semi-circular portion of the micro-machined base enclosure.

A system and method for rehabilitating a patient. In some embodiments, a patient or other subject can stand on a balance plate and perform one or more tests in response to instructions or visual stimuli on a display screen. Data, such as Centre of Pressure data, can be obtained from the balance plate and processed by a processor in tandem with the progression of a test presented on the display screen. The information can be correlated and used to assist in diagnosing or rehabilitating the patient or subject.

A standing posture measuring device provides a comprehensive objective analysis of the standing posture of a patient by measuring shoulder inclination, pelvic tilt in both frontal and sagittal planes, leg length discrepancy, and the difference in weight bearing of each leg. The device includes a base with two vertical side poles extending upward therefrom. The poles include electronic rulers for measuring leg lengths, while the base includes a weight scale for each foot for measuring a difference in weight bearing. The poles also support a shoulder inclination instrument and a pelvic inclination instrument that measures both lateral pelvic tilt and the angle of pelvis tilt in the sagittal plane.

A computer implemented system and method for performing a quantitative balance assessment on a subject are described. The method comprises instructing the subject to perform an assessment task with at least a first kinematic sensor mounted on the subject's head; collecting kinematic sensor measurements, comparing the measurements to a threshold value; summing the total number of kinematic sensor measurements that exceed the threshold value to generate a numerical score indicative of the subject's postural stability. The system makes use of only kinematic data collected from the wireless sensors to quantify BESS, and generate a BESS score, without any other physiological variables such as forward sway.

A method for treating vestibular impairment, comprising: (a) providing a virtual reality apparatus comprising: a headset; a smartphone having a computer processor, a camera, and a display screen; a case holding the smartphone attaching to the headset; a first optical arrangement, for focusing a field of view of the camera on the user's eye; a second optical arrangement for viewing the display screen; and an application and the processor adapted to display a stimulation exercise on the screen; wherein, the application records movements of the eye, during the exercise, using the camera; wherein the application determines user response competence to the exercise, based upon the movements; and accordingly modifies exercise degree of difficulty; (b) placing the headset on the user's head; (c) focusing the field of view onto the eye; (d) displaying the exercise; and (e) recording eye movements, using the camera.

A mental impairment detection system and non-invasive method of detecting mental impairment of a user are provided. A test (e.g., an inhibitory reflex test or a sustained attention test) is administered to the user, brain activity in a frontal lobe of the user is non-invasively detected while the test is administered to the user, and a level of mental impairment of the user is determined based on the brain activity detected in the frontal lobe of the user.

A person's fall risk may be determined based on machine learning algorithms. The fall risk information can be used to notify the person and/or a third party monitoring person (e.g. doctor, physical therapist, personal trainer, etc.) of the person's fall risk. This information may be used to monitor and track changes in fall risk that may be impacted by changes in health status, lifestyle behaviors or medical treatment. Furthermore, the fall risk classification may help individuals be more careful on the days they are more at risk for falling. The fall risk may be estimated using machine learning algorithms that process data from load sensors by computing basic and advanced punctuated equilibrium model (PEM) stability metrics.