An autonomous assistive robot needs to recognize the body-limb posture of the person being assisted while he/she is lying in a bed to provide care services such as helping change the posture of the person or carrying him/her from the bed to a wheelchair. This paper presents a data-efficient classification of human postures when lying in a bed using a hybrid fuzzy logic and machine learning approach. The classifier was trained using a relatively small dataset containing 19,800 annotated depth images collected using Kinect from 32 test subjects lying in bed. An overall accuracy of 97.1% was achieved on the dataset. Furthermore, the image dataset including depth and red-green-blue (RGB) images, is available to the research community with the publication of this paper, with the hope that it can benefit other researchers.INDEX TERMS Human posture, posture recognition, lying in bed, human posture dataset.
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