Implementation of a Fusion Classification Model for Efficient Pen-Holding Posture Detection

Pen-holding postures (PHPs) can significantly affect the speed and quality of writing, incorrect lead to health problems. This paper presents experimentally implements a methodology for quickly recognizing correcting poor writing using digital dot matrix pen. The method first extracts basic handwriting information, including page number, coordinates, movement trajectory, pen tip pressure, stroke sequence, handling time. information is then used generate features that are fed into our proposed fusion classification model, which combines simple parameter-free attention module convolutional neural networks (CNNs) called NetworkSimAM, CNNs, an extension well-known long short-term memory (LTSM) Mogrifier LSTM or MLSTM. Finally, ends with step (Softmax) recognize type PHP. implemented achieves significant results through receiver operating characteristic (ROC) curves loss functions, recognition accuracy 72%, is, example, higher than single-stroke model (i.e., TabNet incorporating SimAM). obtained show promising solution provided accurate efficient PHP has potential improve while reducing problems induced by postures.