Over the last few decades, Smartphone technology has seen significant improvements. Enhancements specific to built-in Inertial Measurement Units (IMUs) and other dedicated sensors of the smartphones(which are often available as default) such as- Accelerometer, Gyroscope, Magnetometer, Fingerprint reader, Proximity and Ambient light sensors have made devices smarter and the interaction seamless. Gesture recognition using these smart phones have been experimented with many techniques. In this solution, a Recurrent Neural Network (RNN) approach, LSTM (Long-Short Term Memory Cells) has been used to classify ten different gestures based on data from Accelerometer and Gyroscope. Selection of sensor data (Accelerometer and Gyroscope) was based on the ones that provided maximum information regarding the movement and orientation of the phone. Various models were experimented in this project, the results of which are presented in the later sections. Furthermore, the properties and characteristics of the collected data were studied and a set of improvements have been suggested in the future work section.

翻译：过去几十年里，智能手机技术取得了显著进步。内置惯性测量单元（IMU）及其他专用传感器（通常作为默认配置）——如加速度计、陀螺仪、磁力计、指纹识别器、接近传感器和环境光传感器——的增强，使设备更加智能化，交互更加无缝。利用这些智能手机进行手势识别已通过多种技术进行了实验。在本方案中，采用循环神经网络（RNN）方法中的LSTM（长短时记忆单元），基于加速度计和陀螺仪的数据对十种不同手势进行分类。传感器数据（加速度计和陀螺仪）的选择依据是提供最大程度关于手机运动和方向信息的数据。本项目中实验了多种模型，其结果将在后续章节中呈现。此外，本文还研究了采集数据的属性和特征，并在未来工作部分提出了一系列改进建议。