Methods and systems for in-bed pose estimation
AssigneeNortheastern University
InventorsSarah Ostadabbas, Shuangjun Liu
Life of this patent
Estimating the term…
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.
Claims20
A non-contact method of estimating an in-bed human pose, comprising 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, said model comprising a machine learning inference model trained on a training dataset of a plurality of in-bed human poses.
A system for estimating a pose of a human subject lying on a bed, comprising:
- a long wavelength infrared camera positioned above the human subject for capturing thermal imaging data of the human subject lying on the bed;
- and
- a computer system coupled to the long wavelength infrared camera, the computer system comprising at least one processor, memory associated with the at least one processor, and a program supported in the memory containing a plurality of instructions which, when executed by the at least one processor, cause the at least one processor to receive the thermal imaging data and process the thermal imaging data using a model to estimate the pose of the human subject, said model comprising a machine learning inference model trained on a training dataset of a plurality of in-bed human poses.
Dependencies are inferred from the claim text.