Abstract: Methods and systems for providing a dataset of human in-bed poses include simultaneously gathered images of in-bed poses of humans from imaging modalities including red-green-blue (RGB) and one or more of long wavelength infrared (LWIR), depth imaging, and pressure mapping. The images are obtained under a lighting condition and a cover condition. The dataset can be used to train a model of estimating human in-bed poses and for methods of estimating human in-bed poses. Methods and systems of estimating three-dimensional human poses from two-dimensional input images are provided.

Abstract: Provided herein are systems and methods for estimating contact pressure of a human lying on a surface including one or more imaging devices having imaging sensors oriented toward the surface, a processor and memory, including a trained model for estimating human contact pressure trained with a dataset including a plurality of human lying poses including images generated from at least one of a plurality of imaging modalities including at least one of a red-green-blue modality, a long wavelength infrared modality, a depth modality, or a pressure map modality, wherein the processor can receive one or more images from the imaging devices of the human lying on the surface and a source of one or more physical parameters of the human to determine a pressure map of the human based on the one or more images and the one or more physical parameters.

Abstract: Systems and methods are provided for in-bed pose and posture determination and tracking for a human subject including an imaging device, the imaging device positioned proximate to a bed and oriented to capture images of the subject lying in the bed, and a processing unit operative to receive the captured images, the captured images including a plurality of image frames, the processing unit including a pose estimation module trained with a dataset of lying poses and operative to estimate poses of the subject lying in the bed based the image frames, and a posture classification module trained with the dataset of lying poses and operative to classify positions of the subject lying in the bed based on the image frames, the processing unit operative to determine a pose and posture of the subject lying in the bed.

Abstract: Methods are provided for estimating a pose of an infant using image analysis and artificial intelligence. A classifier is trained using a dataset containing hybrid synthetic and real infant pose data. Multi-stage invariant representation machine learning strategies are employed that transfer knowledge from adjacent domains of adult poses and synthetic infant images into a fine-tuned domain-adapted infant pose estimation model.

Abstract: Provided herein are systems and methods for estimating contact pressure of a human lying on a surface including one or more imaging devices having imaging sensors oriented toward the surface, a processor and memory, including a trained model for estimating human contact pressure trained with a dataset including a plurality of human lying poses including images generated from at least one of a plurality of imaging modalities including at least one of a red-green-blue modality, a long wavelength infrared modality, a depth modality, or a pressure map modality, wherein the processor can receive one or more images from the imaging devices of the human lying on the surface and a source of one or more physical parameters of the human to determine a pressure map of the human based on the one or more images and the one or more physical parameters.

Abstract: Methods and systems for providing a dataset of human in-bed poses include simultaneously gathered images of in-bed poses of humans from imaging modalities including red-green-blue (RGB) and one or more of long wavelength infrared (LWIR), depth imaging, and pressure mapping. The images are obtained under a lighting condition and a cover condition. The dataset can be used to train a model of estimating human in-bed poses and for methods of estimating human in-bed poses. Methods and systems of estimating three-dimensional human poses from two-dimensional input images are provided.

Abstract: Non-contact methods and systems are disclosed for estimating an in-bed human pose. The method includes the steps of: (a) capturing thermal imaging data of a human subject lying on a bed using a long wavelength infrared camera positioned above the human subject; (b) transmitting the thermal imaging data to a computer system; and (c) processing the thermal imaging data by the computer system using a model to estimate the pose of the human subject, the model comprising a machine learning inference model trained on a training dataset of a plurality of in-bed human poses.

Abstract: Non-contact methods and systems are disclosed for estimating an in-bed human pose. The method includes the steps of: (a) capturing thermal imaging data of a human subject lying on a bed using a long wavelength infrared camera positioned above the human subject; (b) transmitting the thermal imaging data to a computer system; and (c) processing the thermal imaging data by the computer system using a model to estimate the pose of the human subject, the model comprising a machine learning inference model trained on a training dataset of a plurality of in-bed human poses.

Abstract: A centrifugal switch actuator including a junction box, a cover, a first static and dynamic contact combination, a second static and dynamic contact combination, an outer swing arm, an inner swing arm, and a reset elastic element. The first static and dynamic contact combination, the second static and dynamic contact combination, and the reset elastic element are located in the junction box. The outer swing arm includes a first outer swing arm and a second outer swing arm. The inner swing arm includes a first inner swing arm and a second inner swing arm. The length of the first static and dynamic contact combination is different from that of the second static and dynamic contact combination.

Abstract: A centrifugal switch actuator including a junction box, a cover, a first static and dynamic contact combination, a second static and dynamic contact combination, an outer swing arm, an inner swing arm, and a reset elastic element. The first static and dynamic contact combination, the second static and dynamic contact combination, and the reset elastic element are located in the junction box. The outer swing arm includes a first outer swing arm and a second outer swing arm. The inner swing arm includes a first inner swing arm and a second inner swing arm. The length of the first static and dynamic contact combination is different from that of the second static and dynamic contact combination.