An apparatus includes a training area in the form of an enclosed space in which light levels can be controlled, at least one physical element related to a task for which an individual is to be trained, and a lighting arrangement. The lighting arrangement generates a background luminance level in the training area sufficient to cause the vision system of the individual to function in the mesopic or low photopic range of vision, and the physical elements are themselves illuminated by an illumination means. The luminance level of the physical elements is greater than the background luminance level.

Methods for training a model for use in monitoring a health parameter in a person are disclosed. In an embodiment, a method involves monitoring a blood pressure of a person using a control blood pressure monitoring system, receiving control data that corresponds to the monitoring using the control blood pressure monitoring system, receiving stepped frequency scanning data that corresponds to radio waves that have reflected from blood in a blood vessel of the person, wherein the stepped frequency scanning data is collected through multiple receive antennas over a range of frequencies, generating training data by combining the control data with the stepped frequency scanning data in a time synchronous manner, and training a model using the training data to produce a trained model, wherein the trained model correlates stepped frequency scanning data to values that are indicative of a blood pressure of a person.

A method for an expert system to develop fitness training plans includes operating a dynamic exertion system to receive a rate of perceived exertion (RPE) through a user interface of a display device, combines the RPE with a movement, a movement load, and movement repetitions into movement set data, and operates a dynamic exertion algorithm. The method then displays an adjusted movement information display including the prescribed load and the prescribed movement repetitions through the user interface. The dynamic exertion algorithm generates a prescribed load and prescribed movement repetitions, determines a difference in RPE from the expected RPE through operation of a comparator, recalculates the one repetition maximum load value using the calibration and adjustment model when the difference in RPE is greater than an RPE threshold value, and generates a display control comprising the prescribed load and the prescribed movement repetitions.

A system for analysis of user gait and foot disorder intended to minimize risks ulceration and limb amputation associated with the uncontrolled increase of the foot temperature in persons with diabetes. This system comprises a motion, force and temperature sensors and a processing element configured to process motion algorithms, measure ground reaction force (GRF) and changes in foot temperature embedded in the footwear insoles in communication with a smartphone based analysis application using wireless radio interface. The analysis application processes data received from the footwear sensors, compares the results with set of criteria and rules, and if any of the predefined criteria is exceeded, provides alerts to the user and the remote medical supervisor. Additionally, the insoles may be equipped with a haptic actuators configured to determine the level of the user neuropathy by measuring vibration perception threshold (VPT) level.

A method, system, apparatus, and/or device that may include: a first sensor operable to take a first physiological measurement; a second sensor operable to take a second physiological measurement; and a processing device operatively coupled to the first sensor and the second sensor. The processing device may be operable to: receive a first measurement data for the first physiological measurement; receive a second measurement data for the second physiological measurement; generate an event data set based on the first measurement data with the second measurement data; determine an event occurred based on the event data set; determine a type of the event; and in response to the event being a safety event: determine a type of the safety event; identify a risk level of the safety event; and in response to the risk level exceeding a threshold level, notify a second device of the safety event.

A biometric sensor includes a body surface sensor and an e-field signal transmitter. The body surface sensor create a drive-sense signal at a first frequency based on one or more sensing parameters. When operably coupled to a body via one or more electrodes, the body surface sensor provides the drive-sense signal to the body and detects an effect on the drive-sense signal based on electrical characteristics of the body. The body surface sensor generate a data signal based on the detected effect, wherein the data signal represents the body’s electrical characteristics. The e-field signal transmitter generates an outbound signal reference at a second frequency based on the data signal and one or more transmit parameters. The e-field transmitter drives the outbound reference signal to the body, wherein the outbound reference signal is transmitted within at least a portion of the body as an outbound e-field signal at the second frequency.

An Internet of Thing (IoT) device includes a camera coupled to a processor; and a wireless transceiver coupled to the processor. Blockchain smart contracts can be used with the device to facilitate secure operation.

A system for monitoring and analyzing impact data to determine consciousness level and injuries, comprising: impact event monitoring device monitors impact event that occurs to first end-user, impact event monitoring device integrated into objects; impact event monitoring device collects impact data of objects, first end-user using sensors, impact sensing unit, motion detection unit, GPS module, image capturing unit; network module send impact data to impact event monitoring and analyzing module, impact event monitoring and analyzing module convert impact data into Yaw, Pitch and Roll data, impact event monitoring and analyzing module analyze Yaw, Pitch and Roll data with machine learning techniques and deep learning techniques; impact event monitoring and analyzing module report and send emergency notifications to second computing device, impact data accessing module enable second end-user to examine location and intensity of impact event to understand consciousness level and injuries for providing better treatment to first end-user.

An athlete wearing footwear measures jump heights with a motion sensor mounted on the footwear over toes of the athlete. By sensing vertical jump start motions the sensor detects jump start and finish times of −4 g start and −4 g landing. The sensor, a body wearable mems sensor developed by JAWKU, L.L.C., has a previously installed generic factory scale calibration factor. The athlete replaces this calibration factor with a new calibration scale factor selecting an “absolute” external reference device which measures jump height. This device measures several jump heights then inputted to an algorithm app in the sensor to calculate the new calibration scale factor customized to the actual athlete. The motion sensor has built in programming apps to periodically receive an upgraded factory scale calibration factor which upgrade is based on an ever increasing data pool of jump heights. The updated factory calibration factor is then again replaced by the athlete personally taking several new measured jumps which jump heights are in turn inputted to the sensor. The progress made in evolving jumping skills based on training and specific conditioning exercises can thus be motion sensor evaluated.

The present invention measures jump heights using an IMU sensor module slipped in a pocket of a removable side ankle mount clip placed over any low, mid or high tops ankle athletic running shoe. A micro-processor in the IMU sensor module converts analog jump height data collected with real time digital signal processing to digital data sent to specialized algorithms loaded in a RF paired smartphone to refine the digital data to accurately calculate the height of the jump. The clip has two downward spaced legs joined by a curved arch at the top with a first leg being flexible and fitting snugly against a wearer's ankle below the fibula bone with the curved arch resting over the shoe's collar. The second leg has a foot extending outwardly from the curved arch to form a pocket with a top opening to receive and snugly hold the module.