Xsensor Technology Corporation

Ian Main

Timothy Carl Gorjanc

Melissa Elizabeth Remus Jones

Mohammad Najafi

Mitchell Robert Knight

Jeffrey Chao-Hsin Li

Adele Syt Fu Chui

Bruce Malkinson

Robert Miller

Christine J Gonis

Dylan Heckbert

Maxence Petit

Murray Ross Vince

Alexander Wong

Terence Russell

John Alexander Hogg

Terry Russell

Curtis Anderson

Mitch Ousdahl

Dave Jack

David Jack

Kevin Altman

Terrence Russell

Chris Cooper

Stephen Anstey

Michael Reid Ivey

Omolbanin Yazdanbakhsh Poodeh

Madeleine Breen Townley

Tyler Gill

Tim Gorjanc

A weight support device includes a sensor grid that measures pressure data while a user is on the weight support device. The weight support device is connected to a computer that analyzes the pressure data and generates a virtual figure to represent the user. Based on the pressure data, the computer determines how the user moves and adjusts relative positions of segments in the virtual figure that represent various body parts corresponding to the movements of the user. The relative positions of the segments may be determined based on a kinematic model. The virtual figure is presented on a display (e.g., in a video) to illustrate how the user moved.

System and method for generating and visualizing virtual figures from pressure data captured using weight support devices for visualization of user movement

Embodiments may relate to an intelligent patient monitoring system, which may include a weight support device, a computer, and a display. The weight support device supports a patient and includes a sensor grid layer with a plurality of sensors to measure pressure data. The computer predicts a pressure injury outcome and/or a fall outcome based on the pressure data. The pressure injury outcome includes a prediction of risk of the patient developing a pressure injury. The fall outcome includes a prediction of risk of the patient experiencing a fall. The computer may utilize a machine learning model to determine either or both outcomes. The display presents a notification generated based on the pressure injury outcome or fall outcome. The notification indicates that an adjustment of a positioning of the patient is needed to aid in the prevention of the pressure injury or the fall.

Intelligent patient monitoring system

Embodiments may relate to an air-permeable weight support system, which may include a microclimate fabric layer and an air-permeable capacitive sensor layer below the microclimate fabric layer. The air-permeable capacitive sensor layer may include two air-permeable substrates, each carrying a plurality of electrically conductive pathways that are impermeable to air. The two air-permeable substrates may securely position the electrically conductive pathways to define a sensor grid and position the electrically conductive pathways that are impermeable to air to be spaced apart to define a plurality of air pathways for the air-permeable capacitive sensor layer. The sensor data may be used to determine a sleep state of the person using the weight support system. A computer may identify the poses of the person based on the pressure sensor data. A machine learning model may rely on the poses, the heart rates, the respiration rates to determine the sleep state.

Intelligent weight support system

An ISS is a seating system that actively adjusts to improve an occupant's comfort, performance, and safety in a specific driving environment. The ISS determines the occupant's posture, position on the seat surface, and/or physiological state, for example, by applying a machine vision process. The ISS can further determine a driving environment. The ISS adjusts its settings and settings of the vehicle according to one or more factors such as an occupant's posture, the occupant's physiological state, the occupant's preferences, and/or the driving environment. The ISS can include a state machine that determines a current state and determines if a change has occurred such that the system should shift to another state that best suits this change. The ISS makes adjustment according to system settings associated with the best suitable state.

Intelligent seat systems

A seat for a pedal-powered vehicle is described. The seat includes a support frame, a seat element and a nose. The seat element and the nose are implemented as separated components and are supported by the support frame. The seat element is configured to support at least part of a rider's weight. The seat element carries a pressure-relieving region that is configured to be located at a location corresponding to an ischial tuberosity (IT) of the rider, thereby relieving pressure exerted on the rider's IT's.

Bicycle seat for improved comfort, performance, and safety

A seat for a pedal-powered vehicle includes a support frame, a left seat element, a right seat element, and a nose. The left and right seat elements and the nose are implemented as separate components supported by the support frame. The two seat elements support a seated rider's weight while the nose does not. The seat elements and the nose form a gap below the seated rider's perineum area. The seat elements pivot forwards and backwards when the seated rider is pedaling. The seat elements counter-pivot when the seated rider is pedaling. Each seat element includes a concave surface that supports the seated rider.

Bicycle seats

A bedding system uses a convolutional neural network (CNN)-based machine vision to makes adjustments for comfort and/or support. The machine vision process identities a body position by using a trained CNN that receives a pressure image and identifies a body position. The body position may be determined by classifying the pressure image into a predetermined body position classification. The machine vision process includes at least one trained CNN that determines joint locations. The machine vision tracks pressure accumulated at joints over time.

Bedding system with a CNN based machine vision process

Sleep environment control systems adjust sleep environments independently for individual users. A sleep environment control system delivers multiple environment adjustments via one opening to a user supported by a mattress. The environment adjustments include airflow, water vapor, light, and sound. The sleep environment control system can be placed in adjacent to the mattress thereby to deliver the environment adjustments to the user. The environment adjustments are delivered to the user in a localized fashion such that they do not disrupt other users. The sleep environment control system further generates the one or more environment adjustments.

Sleeping environment control systems

An improved sleep system includes an actively responsive bed and mattress combination. The system adjusts the contour and microclimate of the mattress surface, in addition to the ambient conditions of the sleep environment based on the user's preferences and physiological state. A variety of technologies are integrated into the sleep system in order to determine the settings for an improved sleep environment, and automatically adjust the mattress firmness, bed surface temperature, humidity, and/or ambient light and sound.

Intelligent sleep ecosystem

A process makes electrical connections between electrical wires and flexible conductive elements, such as conductive fabrics. The process makes strong electrical connections that are mechanically flexible and can simultaneously create multiple electrical connections. The process involves creating an assembly that includes at least a TPE layer, an electrical wire, an insulating layer, and a flexible conductive element, and applying heat and pressure to the assembly. For example, a conductive fabric is disposed on an insulating layer, a wire is positioned onto a surface of the insulating layer, and a TPE layer is disposed over the wire and overlaps the conductive fabric element. When applying the heat and pressure, the wire melts through the insulating layer to make electrical contact with the conductive fabric element, and the TPE layer conforms to an exterior of the wire and bonds to the insulating layer.

Electrical connection of electrical wires to flexible conductive elements

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Calgary, Canada

Ian Main