Aerial Technologies Inc.

Other

Aerial Technology Inc.

Negar Ghourchian

Michel Allegue Martinez

David Grant

Francois Morel

Pascal Paradis-Theberge

Amir Minayi Jalil

Doina Precup

Sam Heidari

Pauline Tranchant

Raihan Seraj

Azar Zandifar

Qianyu Liu

Mark Hopper

Seyedehsan Bateni

Debarshi Patanjali Ghoshal

Viswanath Pulle

Mathieu Rozon

Shwetha Hymavathi Yadavalli

Vahid Reza Asghari

Systems and methods are provided for human activity analysis and localization using channel state information (CSI). A streamlined data processing and feature extraction approach that addresses concept drifts for time series data is provided. Data obtained from CSI or any other mechanism is used to estimate the wireless channel between two different wireless nodes (e.g., an access point (AP) and an associated station (STA)), and can be used to train a robust system capable of performing room level localization. A phase and magnitude augmented feature space along with a standardization technique that is little affected by drifts is also used.

Handling concept drift in Wi-Fi-based localization

Systems and methods of using wireless signals to create a sensing infrastructure for tracking the location of a moving subject within residential or industrial indoor environments are provided. The changes and disruptions of wireless signals transmitted and received by a plurality of wireless devices are collected and analyzed to infer the position of a moving participant within a sensing area. More particularly, a proximity-based method that models and estimates the location of a moving participant within the sensing area with respect to a single or plurality of fixed position(s) of wireless devices is provided.

Proximity-based model for indoor localization using wireless signals

Wireless device free 'occupancy detection' in residential or small industrial properties is an essential function within the broader scope of smart environments. Applications include, monitoring a subject's, e.g. an elderly person, behaviour for health assessments or moving towards more efficient energy usage in smart homes. Changes and disruption of wireless signals transmitted and received by the plurality of wireless devices are collected and analyzed to infer the presence of a subject the sensing area. More particularly, CSI information over time analysed with one or models can estimate the presence of a subject within the sensing area whether the subject moves or stays still.

Systems and methods for occupancy detection using WiFi sensing technologies

Technologies for attestation techniques, systems, and methods for determining an individual's movement are provided. The individual's movement is determined by passive indoor positioning technology using channel state information (CSI). A first set of data associated with an impulse or frequency response determined using channel state information (CSI) may be received from a Wireless access point located in proximity to a monitored space. A second set of data may also be received from an Internet of Things (IoT) device located in proximity to the monitored space. The first set of data may be compared to the second set of data. A profile database may be updated to replace the first set of data with the second set of data when the first and second sets of data are different.

Self-learning based on Wi-Fi-based monitoring and augmentation

A wireless motion detection system is provided which uses a sharp band-stop and low-pass filters to reject unwanted low and high frequency components of a signal in the CSI. A gap in the frequency domain is caused by taking the absolute value of the signal when using the absolute value of channel matrix H∨ for motion detection. A sharp band-stop (notch) filter is used to reject any unwanted signals that have significant components in this gap. A low-pass filter is used to reject all unwanted signal after the motion highest frequency.

Wireless motion detection using multiband filters

Systems and methods for tracking activities of daily living (ADL) of a user are provided. The method monitors data from one or more internet of things (IoT) devices corresponding to one or more ADLs. The method further monitors data from an activity identification module. The data received is related to an ADL and includes one or more steps specific to the ADL. The data related to the ADL is stored in an activity logging database located in a memory and compared to an activity best practices (BP) database. A score that represents a compliance level is calculated based on the compared data. A response that corresponds the compliance level from a response database is executed.

Monitoring activity using Wi-Fi motion detection

A method of utilizing Wi-Fi based passive motion detection to deliver targeted advertising through a smart TV is provided. The system uses an agent in connection with the wireless access point that the Smart TV uses to access the internet to make motion determinations. The CSI data of the access point is analyzed to identify if a user is present, if the user is stationary, the previous location of the user, and the activity the user is currently engaged in. An advertisement may be selected based on a last visited location and activity. Such advertisements may further be delivered to more engaged viewers when the advertiser pays a higher rate.

Smart media display

The systems and method proposed herein aim to identify a mobile device or devices worn by an individual or a subject that has entered an area monitored by a passive motion detection system that uses wireless signals to sense motion in the space. The system will collect as much signals as possible from both the devices worn by the individual and from the system performing the passive (device-free) motion detection for identifying the individual or person of interest. The individual or person of interest may be a user of a product or an intruder.

System and methods for identifying a subject through device-free and device-oriented sensing technologies

An intelligent entrance detection system is provided where, primarily, any authorized or unauthorized entry to an area of a residential (or small industrial) unit covered by a wireless network is automatically detected from active off-the-shelf devices in that area. After the detection, an identification algorithm is employed to verify if this entry is a legal or illegal action. Based on this verification, either the routine smart home system is activated or a hazardous monitoring period begins to further investigate the suspicious event. If the illegal entrance is confirmed during the hazardous monitoring period, the owner of the property is informed through an intruder alarm protocol. In this invention, all of the analytic and processing steps, including entrance detection, owner identification, and device-free authority verification are designed based on monitoring and quantification of changes in surrounding wireless signals originated by human or object movements within the sensing area.

System and methods for smart intrusion detection using wireless signals and artificial intelligence

Systems and methods for detecting and monitoring human breathing, respiration, and heart rate using statistics about the wireless channel between two or more connected devices. A user is monitored for identifying patterns in the user's behavior that may allow the system to alert a caregiver to deviations in the user behavior that may be indicative of a potential issue, such as depression. A presence may further detected in a sensing area through the detection of spectral components in the breathing frequency range of comprises user includes transforming phase difference between spatial streams and amplitude of the samples representing frequency response of the channel for any frequency value into frequency domain to perform frequency analysis. Statistical analysis may be performed on the frequency space provided by the transformation. Micro motions may also be detected by detecting presence in a sensing area through the detection of spectral components in the micro motion frequency range.

System and method for presence and pulse detection from wireless signals

Device-free localization for smart indoor environments within an indoor area covered by wireless networks is detected using active off-the-shelf-devices would be beneficial in a wide range of applications. By exploiting existing wireless communication signals and machine learning techniques in order to automatically detect entrance into the area, and track the location of a moving subject within the sensing area a low cost robust long-term tracking system can be established. A machine learning component is established to minimize the need for user annotation and overcome temporal instabilities via a semi-supervised framework. After establishing a robust base learner mapping wireless signals to different physical locations from a small amount of labeled data; during its lifetime, the learner automatically re-trains when the uncertainty level rises significantly. Additionally, an automatic change-point detection process is employed setting a query for updating the outdated model and the decision boundaries.

Device-free localization methods within smart indoor environments

System and methods of a mobile application to remotely monitor residents with device-free sensing of human motion over Wi-Fi is provided. A detailed behavioral analysis of various functions related to the sleep and activity of the subject has been provided. The Remote Care Application (RCA) is a mobile application on several platforms that displays the results of all the analysis and behavioral patterns obtained from the motion from the subject. The system relies on existing wireless communication signals and machine learning techniques in order to automatically detect the motion of the subject, sleep analysis of the subject, activity analysis of the subject.

Systems and methods for remote monitoring care using wireless sensing technologies

Systems, and methods for selecting advertisements based on party size and/or engagement are provided. An Internet-connected media display device may be detected as displaying media. An up-to-date party size in a monitored space may be determined based on frequency response data by a wireless access point located close to the space. An up-to-date engagement level may also be determined with respect to each individual in the space based on frequency response data by the wireless access point. An advertisement database may be filtered for a qualifying advertisement based on the identified party size and engagement levels.

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Montreal, Canada

Negar Ghourchian