基于LSTM的智能手机3D手写识别

3D handwriting recognition of smartphone based on LSTM

针对传统传感器需要在特定的空间区域内才能进行人机交互,极易受到外部环境因素干扰的问题,提出一种新的基于长短时记忆神经网络(LSTM)的智能手机3D空间手写识别方法,用于非特定三维空间中实现的人机交互.首先,利用智能手机内置三轴加速度传感器,采集手部运动数据,并将采集的数据进行预处理操作,构建3D手写识别数据集;然后,基于LSTM构建3D手写识别模型,并利用构建的数据集进行训练;最后,利用训练后的模型实现智能手机的3D手写分类识别.通过在本文自建的非依赖用户数据集上进行测试,实验结果表明,该识别方法可以实现86.4%的准确率,88.1%的召回率,88.4%的精准率和88.0%的F_(1)分数.

Traditional sensors are prone to receive the interference of external environmental factors due to achieving human-machine interaction in specific spatial area.3D handwriting recognition of smartphones based on the long short-term memory(LSTM)neural network is proposed,which can be used in human-machine interaction in non-specific 3D spaces.First,three-axis acceleration sensors of smartphones are used to collect data which perform pre-processing operations to construct a 3D handwriting recognition dataset.Then,the 3D handwriting recognition model based on LSTM is constructed and pre-trained by adopting the constructed datasets.Finally,the trained model is applied to implement 3D handwriting classification recognition for smartphones.By testing on a self-built non-dependent user dataset,experimental results show that the proposed model can achieve the accuracy rate of 86.4%,recall rate of 88.1%,precision rate of 88.4%,and F_(1) score of 88.0%.