Knee　osteoarthritis　(KOA)　is　one　of　the　major　causes　of　lower　limb　disability.　This　study　aims　to　develop　a　computer-based　approach　to　discriminate　KOA　individuals　from　controls　by　using　entropy-based　features,　and　therefore　to　provide　an　auxiliary,　quantitative　tool　for　KOA　diagnosis.　The　surface　EMG　(sEMG)　data　were　collected　from　the　vastus　lateralis,　vastus　medialis,　biceps　femoris,　and　semitendinosus　when　KOA　participants　and　controls　were　walking　barefoot　on　ground　at　a　self-paced　speed.　We　employed　and　compared　three　different　entropy　measures,　including　1)　approximate　entropy,　2)　sample　entropy,　3)　fuzzy　entropy,　for　extracting　KOA-related　features　from　the　sEMG　signals　for　classification.　The　differences　between　the　KOA　group　and　healthy　controls　are　primarily　shown　in　the　fuzzy　entropy　features　extracted　from　the　vastus　medialis　and　biceps　femoris　muscle　pair.　Among　all　tested　measures,　the　fuzzy　entropy　yielded　the　best　performance　in　distinguishing　KOA　patients　from　controls,　with　92%　of　accuracy,　91.43%　of　sensitivity　and　93.33%　of　specificity.　The　results　indicate　that　the　fuzzy　entropy　method　is　applicable　for　extracting　KOA-related　features　from　sEMG,　which　can　be　developed　as　a　sensitive　metric　for　computer-assist　diagnosis　of　knee　osteoarthritis.