Smart　environment　is　an　efficient　and　cost-effective　way　to　afford　intelligent　supports　for　the　elderly　people.　Human　activity　recognition　is　a　crucial　aspect　of　the　research　field　of　smart　environments,　and　it　has　attracted　widespread　attention　lately.　The　goal　of　this　study　is　to　develop　an　effective　sensor-based　human　activity　recognition　model　based　on　the　belief-rule-based　system　(BRBS),　which　is　one　of　representative　rule-based　expert　systems.　Specially,　a　new　belief　rule　base　(BRB)　modeling　approach　is　proposed　by　taking　into　account　the　self-　organizing　rule　generation　method　and　the　multi-temporal　rule　representation　scheme,　in　order　to　address　the　problem　of　combination　explosion　that　existed　in　the　traditional　BRB　modelling　procedure　and　the　time　correlation　found　in　continuous　sensor　data　in　chronological　order.　The　new　BRB　modeling　approach　is　so　called　self-organizing　and　multi-temporal　BRB　(SOMT-BRB)　modeling　procedure.　A　case　study　is　further　deducted　to　validate　the　effectiveness　of　the　SOMT-BRB　modeling　procedure.　By　comparing　with　some　conventional　BRBSs　and　classical　activity　recognition　models,　the　results　show　a　significant　improvement　of　the　BRBS　in　terms　of　the　number　of　belief　rules,　modelling　efficiency,　and　activity　recognition　accuracy.