Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method for planning a bone grafting procedure includes the collection of imaging scan data of the target region and of a harvest region as a source for a bone graft complementary to the target region. Virtual bone model and harvest regions are generated. A computer processor determines at least one parameter for a bone graft model complementary to the target region. A location at the harvest region for the bone graft is identified based on the bone graft model. The location of the harvest region is registered to a first computer-assist device and the bone graft is harvested. The target region is registered to the first computer-assist device or another device. The cutting characteristics for the target region are communicated to the first computer-assist device or the other device to receive the bone graft. A surgical system for performing the computerized method is also provided.