The present disclosure provides methods and devices for assessing movement. In some exemplary embodiments, a system for assessing movement is provided. In some embodiments, the system includes a handheld device. In some embodiments, the handheld device includes a plurality of sensors configured to record motion and position data, a body having a plurality of sides, a battery, and a proximal end configured to interact with a touchscreen of a computing device.

A computing device, such as a wearable device, may include at least two electrodes mounted on a body. The computing device may determine an electrical signal associated with a circuit that includes the at least two electrodes and the user. A pressure applied to at least one electrode of the at least two electrodes may be determined from the electrical signal, and at least one function of the computing device may be implemented, based on the pressure.

Analysis of keystroke dynamics performed by an individual can be used for assessment and monitoring of the individual's motor function. Keystroke events related to a user pressing one or more keys on a keyboard or regions on a touch screen may be analyzed to identify a plurality of distributions of keystroke event intervals. The plurality of distributions may be analyzed to identify one or more features indicative of variation among the distributions and indicative of the user's motor function. Monitoring of a user's motor function may include comparing a value for a feature for one plurality of distribution to a second value for the same feature for another plurality of distributions.

Presented are systems and methods for the accurate acquisition of medical measurement data of a body part of patient. To assist in acquiring accurate medical measurement data, an automated diagnostic and treatment system provides instructions to the patient to allow the patient to precisely position a medical instrument in proximity to a target spot of a body part of patient. For a stethoscope examination, the steps may include utilizing object tracking to determine if the patient has moved the stethoscope to a recording site; utilizing DSP processing to confirm that the stethoscope is in operation, utilizing DSP processing to generate a pre-processed audio sample from a recorded audio signal; using machine learning (ML) to determine if a signal of interest (SOI) is present in the pre-processed sample. If SoI is present, using ML to evaluate characteristics in the signal which indicate the presence of abnormalities in the organ being measured.

The biometric and performance monitoring system includes a computer having a mouse and a keyboard electronically coupled thereto. A first sensor is disposed in the mouse, and is electrically coupled to the computer. A second sensor is disposed in the keyboard and is electrically coupled to the computer. The first sensor and the second sensor measure biometric parameters of a user. A computer-readable storage medium is associated with the computer and is adapted for storing the biometric parameters and the performance parameters. The computer measures performance parameters associated with the mouse and with the keyboard. The computer at least one of provides an alert to the user, electronically communicates with a medical service provider, and electronically communicates with a supervising entity responsive to analysis of changes in the biometric parameters and the performance parameters.

Embodiments for intelligent monitoring of a health state of a user by a processor. A health state of a user may be learned while engaged in one or more activities associated with a computing device. One or more mitigating actions may be identified and recommended to implement by the user to minimize one or more possible negative impacts upon the health state of the user while engaged in the one or more activities associated with the computing device.

In some examples, an apparatus can include a handle having a first surface and a second surface, an inflatable bladder located on the second surface, and a sensor located on the apparatus, where inflation of the inflatable bladder causes the sensor to be in contact with an outer surface of a user of the apparatus.

An access control system includes an identification unit having an infrared (IR) transmitter, an IR detector that receives reflected IR radiation from one or more body parts of the user, one or more signal processing components to determine a temperature of the user, a hand sanitization unit that sanitizes hands of the user when the hands are placed in a palm placement area, and an identification device to receive identification information of the user. The access control system also includes a processor that receives the reading of the user, instructs a door lock controller to unlock a door when the temperature is below the threshold temperature or the temperature is within the temperature range. The processor sends an alert when the temperature is above the threshold temperature or the temperature is outside the temperature range, and sends identification information of the user to one or more network devices.

Disclosed are a system and method of detection of an interaction-error. The interaction-error is derived from an incorrect decision and is directed to interacting with a machine. During human-machine interaction, command related data values are obtained. Command related data values characterize any one of an interacting-command and an interacting-action. The command related data values are compared with command related reference data values, and an interaction-error is identified if a difference between the command related data values and the command related reference data values complies with a predefined criterion.

Methods, systems, and devices for illness detection are described. A method may include receiving heart rate variability (HRV) data associated with a user from a wearable device, the HRV data collected via the wearable device throughout a first time interval and a second time interval subsequent to the first time interval. The method may include inputting the HRV data into a classifier, and identifying a satisfaction of deviation criteria between a first subset of the HRV data collected throughout the first time interval and a second subset of the HRV data collected throughout the second time interval. The method may include causing a graphical user interface (GUI) of a user device to display an illness risk metric for the user based on the satisfaction of the deviation criteria, the illness risk metric associated with a relative probability that the user will transition from a healthy state to an unhealthy state.