Sensor-based　activity　recognition　(AR)　is　a　core　problem　with　the　research　domain　of　smart　environments.　It　has,　however,　the　potential　to　provide　solutions　to　address　the　problems　associated　with　the　growing　size　and　ageing　profile　of　the　global　population.　The　work　presented　within　this　paper　focuses　on　the　extended　belief　rule-based　system　(EBRBS),　which　offered　promising　performance　compared　with　popular　benchmark　AR　models　and　exhibited　a　high　robustness　in　the　situation　of　sensor　failure.　Nevertheless,　efficiency　remains　one　of　the　major　issues　to　be　improved　for　determining　and　updating　the　extended　belief　rule　base　(EBRB)　within　the　EBRBS.　This　is　critical　for　further　utilizing　the　EBRBS　in　AR　situations　within　dynamic　smart　environments.　An　eigendecomposition-based　sensor　selection　method　is　firstly　proposed　to　select　an　effective　subset　of　sensors　and　to　also　enable　efficient　implementation　to　facilitate　online　AR.　A　novel　domain　division-based　rule　generation　method　is　also　proposed　to　generate　and　update　an　EBRB　efficiently　when　new　sensor　data　are　available　or　when　some　sensors　should　be　included　or　excluded　in　the　EBRB.　The　combination　of　these　two　methods　leads　to　an　enhanced　EBRBS,　called　online　updating　EBRBS.　Two　datasets　(in　a　balanced　class　situation)　obtained　from　simulation　and　actual　environments　are　studied　to　provide　detailed　experimental　analysis　as　a　preliminary　study　and　basis　to　handle　further　the　imbalanced　situation　of　real　AR.　The　experimental　results　demonstrate　an　enhanced　performance　of　the　online　updating　EBRBS　compared　with　the　original　EBRBS　and　some　benchmark　AR　models,　in　terms　of　efficiency　and　effectiveness.