Careband, Inc.

Adam G Russek-Sobol

Peter Nanni

Paul C Sheldon

John D Reed

Jon G Ledwith

Patrick J McVey

Brian A Donlin

Joseph T Kreidler

Ross D Moore

Dwayne D Forsyth

Todd Sobol

A tracking system and a method of tracking an object. Wireless signal communication between a central processing unit, a device and numerous wireless transmitters allows the tracking system to provide tracking information through different communication modalities. In one form, the communication is between the device and a first of the wireless transmitters or between the device and one more second wireless transmitters when the device is within Bluetooth® Low Energy wireless signal communication range. Likewise, the communication is between the device and one or more of the first wireless transmitter, the second wireless transmitters or the central processing unit over one of numerous other wireless communication technologies when the device is beyond Bluetooth® Low Energy wireless signal communication range of the first and second wireless transmitters. Additionally, some of the wireless transmitters may form either direct or indirect wireless signal communication with one another. Other components, such as smartphones or related mobile computing devices, may be included in the tracking system.

Wireless devices, systems and methods for object tracking

A wearable electronic device, a system and methods of monitoring with a wearable electronic device. The device includes a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources, as well as a wireless communication sub-module to selectively transmit an LPWAN signal to provide location information based on the acquired data. The device may also include one or more sensors to collect one or more of environmental data, activity data and physiological data. The device may transmit some or all of its acquired data to a larger system, including a cloud-based server to, in addition to providing location-based data, be used as a part of a predictive health care protocol to correlate changes in acquired data to salient indicators of the health of a wearer of the device. In one form, the predictive health care protocol uses a machine learning model.

Wearable electronic device and system for tracking location and identifying changes in salient indicators of patient health

Disclosed are apparatuses, systems, and methods for tracking patients that suffer from dementia. The disclosed apparatus is a wearable device capable of micro-tracking through Bluetooth Low Energy technology and capable of macro-tracking through GPS technology. The device may additionally include sensors to monitor other information such as the health of the patient or the patient's surrounding environment. The disclosed systems utilize the disclosed device in an overall system for tracking patients. These systems teach how the device interacts with the other components of the system (e.g., signal beacons, wireless transmitters, a central processing unit, mobile computing devices) to provide an integrated system to tracking the location and monitoring the well being of the patient. Finally, methods for tracking patients that use the disclosed devices and systems are disclosed.

Wireless devices, systems and methods for long range radio frequency location tracking of objects

A mobile edge computing system, a communication network and a method of using a mobile edge computing device. The mobile edge computing system includes a communication module that establishes signal communication over numerous wireless communication protocols at least one of which uses a low power wide area network protocol. A logic device is made up of a distributed set of processing units including both a central processing unit and one or more processors configured for performing machine learning operations the latter of which include one or more of a graphical processor unit and a tensor processing unit. When the system receives event data that has been acquired by one or more of the communication module and a sensor, the system executes a trained machine learning model and conveys, using the low power wide area network protocol, an output that has been produced by the trained machine learning model.

Edge computing system with low power wide area network connectivity and autonomous or semi-autonomous machine learning

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Chicago, IL, United States of America

Adam G Russek-Sobol