In　online　learning,　imaging　photoplethysmography　(iPPG)　can　extract　biometric　information　from　the　students’　face　video,　which　can　be　used　for　further　analysis　of　learning　status.　However,　the　iPPG-based　methods　cannot　detect　the　heart　rate　accurately　when　the　face-camera　distance　changes　or　the　subject＇s　face　is　occluded　by　the　hand　(Hand-Over-Face,　HOF).　This　paper　proposes　a　distance-adaptive　heart　rate　detection　model　based　on　chroma　and　pixel　distance,　which　can　detect　heart　rate　accurately　with　face-camera　distance　changing　in　a　certain　range.　The　influence　factors　of　distance　are　verified　on　the　region　of　interest　(ROI)　of　the　image　in　the　process　of　iPPG　signal　extraction,　and　the　relationship　model　is　established　between　the　pixel　distance　characteristic　parameters　and　the　physiological　parameters　through　the　BP　neural　network.　The　experimental　results　show　that　the　RMSE　of　the　model　proposed　in　this　paper　is　1.50　bpm,　and　the　model　determination　coefficient　is　0.9504,　which　can　effectively　correct　the　heart　rate　detection　error　caused　by　face-camera　distance　changes.　This　paper　proposes　a　gesture　segmentation　method　GROI　combined　with　gesture　recognition　to　obtain　an　effective　face　ROI　when　HOF　occurs.　The　experimental　results　show　that　when　the　ROI　is　occluded　severely　by　the　gesture,　the　RMSE　for　heart　rate　detection　is　reduced　by　1.11　bpm,　which　demonstrates　the　effectiveness　of　the　algorithm.　©　2023　Elsevier　Ltd