As　the　ageing　population　increases,　it　is　essential　to　manage　human　resources　with　residual　life　prediction,　especially　for　the　ageing　population.　Thus,　this　study　aims　to　predict　residual　life　using　the　available　information　of　health　statuses,　such　as　the　performances　in　activities　of　daily　living　(ADL)　and　memory.　In　this　study,　the　principal　components　of　ADL　and　memory　information　are　extracted　for　prediction.　The　relationship　between　the　principal　components　and　residual　life　is　established　based　on　the　concept　of　the　proportional　residual,　which　states　that　the　residual　life　may　be　proportional　to　the　changes　in　ADL　and　memory　performance.　A　recursive　model　for　residual　life　prediction　is　formulated　and　fitted　for　the　Chinese　Health　and　Nutrition　Survey　data.　Finally,　a　goodness-of-fit　test　is　conducted,　and　an　example　case　is　presented.　The　results　show　that　the　fitted　model　in　this　study　is　more　accurate　and　precise　than　the　original,　unfitted　model　to　estimate　the　residual　life　of　human　beings.　This　study　is　advantageous　over　existing　studies　in　two　aspects:　(1)　the　model　is　formulated　using　a　recursive　method　based　on　stochastic　filtering;　(2)　the　data　include　both　physical　status　and　mental　status.