A　feature　subset　discernibility　hybrid　evaluation　method　using　Fisher　score　based　on　joint　feature　and　support　vector　machine　is　proposed　for　the　feature　selection　problem　of　the　upper　limb　rehabilitation　training　motion　of　Brunnstrom　4-5　stage　patients.　In　this　method,　the　joint　feature　is　introduced　to　evaluate　the　discernibility　between　classes　due　to　the　joint　effect　of　both　candidate　and　selected　features.　A　feature　subset　search　strategy　is　used　to　search　a　set　of　candidate　feature　subsets.　The　Fisher　score　based　on　joint　feature　method　is　used　to　evaluate　the　candidate　feature　subsets　and　the　best　subset　is　selected　as　a　new　selected　feature　subset.　From　these　selected　subsets　such　as　obtained　by　the　above　process,　the　subset　with　the　best　performance　of　support　vector　machine　classification　is　finally　selected　as　the　optimal　feature　subset.　Experiments　were　carried　out　on　the　upper　limb　routine　rehabilitation　training　samples　of　the　Brunnstrom　4-5　stage.　Compared　with　both　the　F-score　and　the　discernibility　of　feature　subset　methods,　the　experimental　results　show　the　effectiveness　and　feasibility　of　the　proposed　method　which　can　obtain　the　feature　subsets　with　higher　accuracy　and　smaller　feature　dimension.