The　goal　of　this　study　was　to　recognize　human　standing　postures　in　human-robot　collaborations　such　that　the　robot　can　serve　the　human　operator　better.　An　intelligent　sensing　floor　was　developed　based　on　a　thin-film　pressure　sensor　and　a　human　standing　posture　dataset　was　obtained　by　transforming　the　pressure　data　into　a　pressure　image.　A　human　standing　posture　recognition　method　based　on　an　improved　convolutional　neural　network　is　proposed.　The　results　of　the　experiments　demonstrate　that　a　convolutional　neural　network　can　be　used　in　the　field　of　pressure　images.　The　proposed　method　returned　a　recognition　rate　of　96.6%.　Compared　to　the　traditional　neural　network,　the　improved　convolutional　neural　network　model　has　better　performance.　The　study　results　are　expected　to　be　used　in　standing　posture　monitoring　to　provide　additional　data　for　a　robot　in　a　human-robot　collaboration　system.　©　2020　-　IOS　Press　and　the　authors.　All　rights　reserved.