Nicola Stelian, Chirila Oana Sorina, Stoicu-Tivadar Lacramioara
University Politehnica Timisoara, Romania, Department of Automation and Applied Informatics.
Stud Health Technol Inform. 2019 Jul 4;262:320-323. doi: 10.3233/SHTI190083.
This paper presents an improved solution for detecting gestures with a better precision using the Leap Motion sensor and Machine Learning support. A neural network is trained to recognize a hand rotation gesture expressing the grade of recovery, with a supination and pronation exercise. The supination-pronation movement is divided into 4 levels because the users are not usually able to perform a complete rotation gesture in hand recovery after injury. The neural network is trained with data representing the hand rotation angle measurements on the x, y and z axes. The Neural Network training is based on the Tensorflow library. 3 tests were carried out to test the network and eventually a 96% gesture-detection accuracy was achieved.
本文提出了一种改进的解决方案，利用Leap Motion传感器和机器学习支持，以更高的精度检测手势。训练一个神经网络来识别表示恢复程度的手部旋转手势，通过旋前和旋后练习。旋前-旋后运动分为4个级别，因为受伤后手恢复时用户通常无法完成完整的手部旋转手势。神经网络使用表示x、y和z轴上手部旋转角度测量的数据进行训练。神经网络训练基于Tensorflow库。进行了3次测试来测试该网络，最终实现了96%的手势检测准确率。
