A wearable device and system has been developed to help users learn the practices of diaphragmatic breathing and breathing patterns to improve running and walking performance. The wearable has a breathing sensor (breathe in-breathe out) and a movement sensor used to identify foot strikes. A processor computes the number of foot strikes occurring while inhaling and the number of foot strikes occurring while exhaling to report breathing patterns as a function of time. The algorithms may be modified to teach breathing patterns to athletes in other sports and deep breathing for numerous movement and minimal movement applications.

There is provided a system for determining severity of an impact and/or a post impact risk score for a subject, the system comprising an accelerometer configured to output signals indicative of movement of the subject along one or any combination of an x-axis, a y-axis, and a z-axis; a magnetometer configured to output signals indicative of variations in position of the subject in a space defined by the x-axis, the y-axis, and the z-axis; and a gyroscope configured to output signals indicative of angular velocity of the subject around one or any combination of the x-axis, the y-axis, and the z-axis; and a processor configured to receive the output and analyse the output signals to determine for the subject one or any combination of Impact Force, Stun Time, Sway Time, Slow Time and Sway Score, wherein the x-axis is a horizontal axis to the ground directed forward of the subject's body; the y-axis being a horizontal axis to the ground directed laterally of the subject's body; and the z-axis is vertical axis to ground. Also provided are methods to determine a post impact risk score for a subject.

Aspects of automatically detecting periods of sleep of a user of a wearable electronic device are discussed herein. For example, in one aspect, an embodiment may obtain a set of features for periods of time from motion data obtained from a set of one or more motion sensors in the wearable electronic device or data derived therefrom. The wearable electronic device may then classify the periods of time into one of a plurality of statuses of the user based on the set of features determined for the periods of time, where the statuses are indicative of relative degree of movement of the user. The wearable electronic device may also derive blocks of time each covering one or more of the periods of time during which the user is in one of a plurality of states, wherein the states include an awake state and an asleep state.

A monitoring apparatus for a human body includes a node network with at least one motion sensor and at least one acoustic sensor. A processor is coupled to the node network, and receives motion information and acoustic information from the node network. The processor determines from the motion information and the acoustic information a source of acoustic emissions within the human body by analyzing the acoustic information in the time domain to identify an event envelope representing an acoustic event, determining a feature vector related to the event envelope, calculating a distance between the feature vector and each of a set of predetermined event silhouettes, and identifying one of the predetermined event silhouettes for which the distance is a minimum.

A cue processor uses one or more sensors to obtain motion data for a user performing a physical task in an environment. A cueing law is based on a model determined from the motion data, for example a movement and skill model where the collected motion data are parsed into one or more movement units used to accomplish a range of outcomes. The cue processor generates a movement phase estimation to predict a movement phase and associated movement feature, and applies the cueing law to generate a cue signal. The cue signal is communicated to the user as a visual, audio or haptic stimulus, selected to target the feature for the user to achieve or improve a desired outcome.

A system for aiding a subject development of physical, mental, and/or emotional skills, subject awareness of their mind state, body state, and/or emotional state, and providing subject biofeedback includes subject-internal signal sensing devices wearable by the subject for sensing signals generated internal to the subject's body; subject-external signal sensing devices for sensing signals generated external to the subject's body; a local computing unit configured for authenticated wireless communication with the subject-internal and subject-external signal sensing devices, and presenting particular types of mind state, body state, and emotional state information to the subject, for instance, in the form of biofeedback (e.g., mind-body state biofeedback, and/or mind-body-emotion state biofeedback); and a cellular network communication unit configured for communicating data corresponding to sensed subject-internal signals and sensed subject-external signals to at least one server by way of at least one cellular network.

A psychological evaluation device that estimates interest of a subject in a content used integrally with a terminal held by the subject. Acceleration data obtained by an acceleration sensor built in the terminal is acquired, and a frequency analysis is performed on the acquired acceleration data to obtain acceleration in the gravity direction of the terminal. By obtaining the acceleration in the gravity direction of the terminal, it is possible to, for example, estimate the subject's interest in the content based on the integral value of each frequency component of the acceleration in the gravity direction obtained by performing the frequency analysis.

The present disclosure relates to method and system for signal processing rehabilitation exercise signals. The method comprises the step of receiving a first and a second motion signals associated with movements of a body part, wherein the motion signals comprise temporal data of the movements. The method further comprises the step of segmenting each of the first and second motion signals into a plurality of segmented signals based on gradients of the motion signals, wherein each segmented signal has consistent gradient. The method further comprises the step of automatically modifying the segmented signals to form multiple combinations of matching signals with similar gradients between the first and second motion signals, such that the first and second motion signals are in one-to-one correspondence. The method further comprises the step of extracting corresponding time intervals of the matching signals in the correspondences.

Medical devices coupled to a sensor, and systems including such devices, can generate data and analysis based on that data, which may be used to identify and/or address problems associated with the implanted medical device, including incorrect placement of the device, unanticipated degradation of the device, and undesired movement of the device. Also provided are medical devices coupled to a sensor, and devices and methods to address problems that have been identified with an implanted medical device.

Aspects of the present invention relate to providing assistance to medical professionals during the performance of medical procedures through the use of technologies facilitated through a head-worn computer.