Bed occupancy detection using geometric features from point clouds

An apparatus for determining if a bed inside an operating room (OR) is occupied by a patient, the apparatus comprising:

• one or more processors;
• a memory coupled to the one or more processors, the memory storing instructions that, when executed by the one or more processors, cause the apparatus to:
• segment a three-dimensional (3D) point cloud of a bed object within a depth image captured in an OR into a plurality of segments in both a length direction and a width direction of the bed object, by i) segmenting the 3D point cloud along a length direction of the bed object into a first plurality of segments, and ii) segmenting the 3D point cloud along a width direction of the bed object into a second plurality of segments being two equal-sized segments;
• extract a set of geometric features from the plurality of segments;
• and
• apply a binary classifier to the set of geometric features to classify the bed object as either being occupied by a patient or not being occupied by a patient.

A system for determining if a bed inside an operating room (OR) is occupied by a patient, the system comprising:

• one or more depth cameras installed in the OR;
• one or more processors coupled to the one or more depth cameras;
• and
• a memory coupled to the one or more processors, the memory storing instructions that, when executed by the one or more processors, cause the system to:
• segment a three-dimensional (3D) point cloud of a bed object within a depth image captured in an OR into a plurality of segments in both a length direction and a width direction of the bed object;
• extract a set of geometric features from the plurality of segments;
• and
• apply a binary classifier to the set of geometric features to classify the bed object as either being occupied by a patient or not being occupied by a patient, wherein the binary classifier comprises a machine learning model trained using a training dataset constructed from a set of depth-camera videos that capture operating room workflow, and the training dataset includes a first class of labeled samples of occupied bed scenarios and a second class of labeled samples of non-occupied bed scenarios, wherein the non-occupied bed scenarios include both empty bed scenarios and non-human-object occupied bed scenarios.