Rutgers, the State University of New Jersey

Stevens Institute of Technology

The State University of New York

Indiana University

The Uab Research Foundation (the University of Alabama at Birmingham)

Yingying Chen

Yan Wang

Marco Gruteser

Jie Yang

Jian Liu

Richard P Martin

Chen Wang

Hongbo Liu

Nitesh Saxena

A system includes a device that is disposed within an environment and is adapted to communicate over a radio frequency communication link. The system also includes a wireless access point disposed within the environment, including a wireless transceiver in communication with the device over a radio frequency communication link using a plurality of channels, and recording a channel state information data set for the radio frequency communication link. The system also includes a monitoring device including a memory storing a plurality of activity profiles, each of which includes an activity and a channel state information profile corresponding to the activity, and a processor receiving, from the wireless access point, the channel state information data set and determining, based on a comparison of the channel state information data set to the channel state information profile of each of the plurality of activity profiles, the activity of the person in the environment.

Device-free activity identification using fine-grained WiFi signatures

A system and method for detecting a suspicious object, including a wireless signal transmitter with first and second transmitter antennas, a first wireless signal receiver on an opposite side of the object from the transmitter having first and second receiver antennas, and a second wireless signal receiver on a same side of the object as the transmitter having a third receiver antenna. The transmitter may emit wireless signals from each of the transmitter antennas. The signals emitted by the first transmitter antenna may be received at the first and second receiver antennas. The signals emitted by both transmitter antennas may be received at the third receiver antenna. The object's material type may be determined based on channel state information of the wireless signals received at first receiver. A size of the object may be determined based on channel state information of the wireless signals received at the second receiver.

In-baggage object detection using commodity Wi-Fi

A user input system comprising a vibration motor, a sensor and a processor in communication with the sensor. The vibration motor is in contact with a surface and generates vibrations in the surface. The vibrations can be altered by a user touching the surface to create altered vibrations. The sensor is in contact with the surface and detects the altered vibrations. The processor receives and analyzes data corresponding to the altered vibrations. The processor determines, based on the analyzed data, whether a user's touch on the surface matches a stored vibration profile of the user.

Systems and methods for user input and authentication using vibration analysis

Device-free activity identification using fine grained WiFi signatures

A method for determining the position in a vehicle of a first device in communication with a microprocessor may include receiving, at the microprocessor, a first set of inertial data from at least one sensor of the first device; receiving, at the microprocessor, a second set of inertial data from at least one sensor of a reference device disposed within the vehicle; and determining, using the microprocessor, the position of the first device in the vehicle by comparing the first set of inertial data with the second set of inertial data.

Systems and methods for detecting driver phone operation using vehicle dynamics data

A method of tracking a service queue using a single-point signal monitor in which at least one customer carries a wireless device may include: receiving a received signal strength from the wireless device as a trace; identify first segments of the trace containing mostly positive sloped received signal strength; identify second segments of the trace containing mostly negative sloped received signal; extract attributes from the first and second segments as child variables for two parent nodes in a Bayesian Network in which the two parent nodes respectively model a beginning of service and a leaving period for a service queue; and inputting the extracted attributes into Bayesian Network; and determining a beginning of service and leaving period using the Bayesian Network. The method may also include determining critical time points in a trace using a K-L divergence technique.

Tracking service queues using single-point signal monitoring

Systems and methods for detecting driver phone operation using device position and orientation data

Growing community of inventors

Morganville, NJ, United States of America

Yingying Chen