At&t Intellectual Property I, L.p.

Shaoda Yu

Nadia Morris

Mohamed Ahmed Mohamed Elmahdy

Steven Todd Barth

Duane Michael Scott Johnson

Michael Osborne Isom

Mohamed Mohamed

David R Eguren

In one example, the present disclosure describes a device, computer-readable medium, and method for monitoring a patient's medication adherence. In one example, an apparatus includes a set of contact pins to electrically engage a conductive trace circuit of a unit dose packaging and to generate a signal in response to a break in the conductive trace circuit, a processor comprising a plurality of analog channels to detect and record the signal, a cellular modem to send, via a data network to which the apparatus is connected, information about the signal to a medication adherence monitoring service, a housing containing the set of contact pins, the processor, and the cellular modem, wherein the housing is configured for removable insertion of the unit dose packaging, and a lock to reversibly lock the unit dose packaging in the housing.

Medication adherence monitoring

Aspects of the subject disclosure may include, for example, obtaining a first plurality of circumference measurements, each of the first plurality of circumference measurements corresponding to a first circumference around a limb of a person at a respective one of a plurality of locations of the limb, each of the first plurality of circumference measurements being obtained from a respective one of a plurality of elastic measurement elements that is positioned at a respective one of the locations; determining, based upon the first plurality of circumference measurements, a first geometric profile along a length of the limb; and outputting data representing the first geometric profile. Other embodiments are disclosed.

Method and apparatus for limb circumference measurement

A method, a computer-readable storage device, and an apparatus for predicting a fall are disclosed. In one example, a method performed by a processor deployed in a communications network includes collecting gait information associated with a user from a first wearable device worn by the user, collecting electroencephalography information associated with the user from a second wearable device worn by the user, calculating a likelihood that the user will fall within a threshold period of time from a current time, wherein the calculating is based on a combination of the gait information and the electroencephalography information; and sending an instruction to an endpoint device associated with the user when the likelihood exceeds a predefined threshold, wherein the instruction instructs the endpoint device to generate an alert alerting the user that a fall is likely.

Fall prediction based on electroencephalography and gait analysis data

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Katy, TX, United States of America

Shaoda Yu