In recent years, low back pain rehabilitation exercises have been widely performed for spine-related illnesses. To facilitate rehabilitation exercises, pose-based human action recognition technique is used to determine human movement from simple videos. Herein, we propose a new stretching side pose classification system using three-layer long short-term memory (LSTM) that can be used in rehabilitation therapy systems. Four types of rehabilitation treatment exercises are selected: bird dog, cat camel, cobra stretch, and pelvic tilt. Features selected based on the high frequency of use for each exercise resulted in improved classification. Consequently, the recognition rate of the selected feature is 97.50%, as classified by the three-layer LSTM model.

近年来，针对脊柱相关疾病广泛进行了腰背痛康复锻炼。为了促进康复锻炼，基于姿势的人体动作识别技术用于从简单视频中确定人体运动。在本文中，我们提出了一种新的拉伸侧姿分类系统，该系统使用三层长短期记忆（LSTM），可以在康复治疗系统中使用。选择了四种类型的康复治疗练习：鸟狗，猫骆驼，眼镜蛇伸展和骨盆倾斜。基于每次练习的高频率选择的功能可以改善分类。因此，按三层LSTM模型分类，所选特征的识别率为97.50％。