Sensor-based　human　activity　recognition　is　now　well　developed,　but　there　are　still　many　challenges,　such　as　insufficient　accuracy　in　the　identification　of　similar　activities.　To　overcome　this　issue,　we　collect　data　during　similar　human　activities　using　three-axis　acceleration　and　gyroscope　sensors.　We　developed　a　model　capable　of　classifying　similar　activities　of　human　behavior,　and　the　effectiveness　and　generalization　capabilities　of　this　model　are　evaluated.　Based　on　the　standardization　and　normalization　of　data,　we　consider　the　inherent　similarities　of　human　activity　behaviors　by　introducing　the　multi-layer　classifier　model.　The　first　layer　of　the　proposed　model　is　a　random　forest　model　based　on　the　XGBoost　feature　selection　algorithm.　In　the　second　layer　of　this　model,　similar　human　activities　are　extracted　by　applying　the　kernel　Fisher　discriminant　analysis　(KFDA)　with　feature　mapping.　Then,　the　support　vector　machine　(SVM)　model　is　applied　to　classify　similar　human　activities.　Our　model　is　experimentally　evaluated,　and　it　is　also　applied　to　four　benchmark　datasets:　UCI　DSA,　UCI　HAR,　WISDM,　and　IM-WSHA.　The　experimental　results　demonstrate　that　the　proposed　approach　achieves　recognition　accuracies　of　97.69%,　97.92%,　98.12%,　and　90.6%,　indicating　excellent　recognition　performance.　Additionally,　we　performed　K-fold　cross-validation　on　the　random　forest　model　and　utilized　ROC　curves　for　the　SVM　classifier　to　assess　the　model’s　generalization　ability.　The　results　indicate　that　our　multi-layer　classifier　model　exhibits　robust　generalization　capabilities.　©　2023　by　the　authors.