摘要
文章结合陀螺仪(GYRO)、加速度计(ACC)、弯曲可变电阻三者收集到的手势动作信息,提出一种融合多传感器信息的手势识别方法,以提高训练及识别的速度。首先执行运动窗口检测,然后平滑滤波,将手势加速信号预处理为帧,最后构建离散隐马尔可夫模型以实现手势识别。
This article combines gesture motion information monitored by three low-cost sensors including gyroscope(GYRO), accelerometer(ACC), and curved variable resistor, and proposes a gesture recognition method for multiple sensor information to improve training and training speed. Firstly, detect the motion window, then smooth filter, preprocess the acceleration signal into frames, and finally construct the discrete Hidden Markov model to realize gesture recognition.
作者
马洁
Ma Jie(School of Physics and Technology,Central China Normal University,Wuhan 430079,China)
出处
《无线互联科技》
2020年第7期53-54,共2页
Wireless Internet Technology
关键词
手势识别
隐马尔可夫
多传感器融合
gesture recognition
Hidden Markov
fusion of multiple sensor