Fall　detection　is　of　great　significance　to　the　elderly.　The　wearable　fall　detection　device　can　collect　real-time　data　to　identify　falling　events,　thereby　helping　to　protect　the　elderly　from　suffering　further　injuries.　The　limited　sensor　data　from　older　adults　in　the　SisFall　dataset　is　insufficient　for　training　a　fall　detection　classifier　that　is　specifically　tailored　to　older　adults.　This　paper　proposes　a　fall　detection　method　based　on　online　transfer　learning.　The　method　uses　the　weighted　online　sequential　extreme　learning　machine　with　a　forgetting　factor　as　an　online　classifier,　which　can　effectively　update　the　model　in　real　time　based　on　continuously　collected　data,　thereby　improving　the　accuracy　of　fall　detection　among　elder　adults.　By　dynamically　updating　the　combination　weights　of　offline　classifiers　and　online　classifiers　to　transfer　source　domain　knowledge　to　the　target　domain,　the　proposed　method　improves　classification　accuracy　in　the　target　domain.　Moreover,　we　incorporate　concept　drift　detection　to　adapt　to　changes　in　the　data　distribution　over　time.　Experimental　results　show　that　the　improved　algorithm　has　a　higher　online　accuracy.　©　2023　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.