Wearable　sensor-based　HAR　(human　activity　recognition)　is　a　popular　human　activity　perception　method.　However,　due　to　the　lack　of　a　unified　human　activity　model,　the　number　and　positions　of　sensors　in　the　existing　wearable　HAR　systems　are　not　the　same,　which　affects　the　promotion　and　application.　In　this　paper,　an　information　gain-based　human　activity　model　is　established,　and　an　attention-based　recurrent　neural　network　(namely　Attention-RNN)　for　human　activity　recognition　is　designed.　Besides,　the　attention-RNN,　which　combines　bidirectional　long　short-term　memory　(BiLSTM)　with　attention　mechanism,　was　tested　on　the　UCI　opportunity　challenge　dataset.　Experiments　prove　that　the　proposed　human　activity　model　provides　guidance　for　the　deployment　location　of　sensors　and　provides　a　basis　for　the　selection　of　the　number　of　sensors,　which　can　reduce　the　number　of　sensors　used　to　achieve　the　same　classification　effect.　In　addition,　experiments　show　that　the　proposed　Attention-RNN　achieves　F1　scores　of　0.898　and　0.911　in　the　ML　(Modes　of　Locomotion)　task　and　GR　(Gesture　Recognition)　task,　respectively.