Pressure mapping smart textile is a new type of sensing modality that transforms the pressure distribution over surfaces into digital ”image” and ”video”, that has rich application scenarios in Human Activity Recognition (HAR), because all human activities are linked with force change over certain surfaces. To speed up its application exploration, we propose a toolkit named LwTool for the data processing, including: (a) a feature library, including 1830 ready-to-use temporal and spatial features, (b) a hierarchical feature selection framework that automatically picks out the best features for a new application from the feature library. As real-time processing capability is important for instant user feedback, we emphasize not only on good recognition result but also on reducing time cost when selecting features. Our library and algorithms are validated on Smart-Toy and Smart-Bedsheet applications, an 89.7% accuracy for Smart-Toy and an 83.8% accuracy for Smart-Bedsheet can be achieved (10-fold cross-validation) using our feature library. Adopting the feature selection algorithm, the processing speed is increased by more than 3 times while maintaining high accuracy for both two applications. We believe our method could be a general and powerful toolkit in building real-time recognition software systems for pressure mapping smart textile.