Nowadays, Sport-related movement recognition plays an essential part in the wellbeing of people's lives. The mention of human movements and gestures is often studied in sports to help analyze, guide, and evaluate activity. The automatic detection of sports-related signals helps find the injuries or indirect physical issues in the human body. Action recognition patterns with complicated motion status and periodicity in sports games can help to more accurately estimate the duration of successful action states. Actions are recognized by identifying the activity in a clip. Quality evaluation of action assigns a quantitative score based on the performance of the action. Based on the score, the action states are analyzed. The main issue of sports game identification correctly tracks the behavior of sportspeople. In this paper, Long Short Term Memory networks (LSTM) with a Bio-inspired Algorithm (BIA) framework have been proposed to recognize the action of a sportsperson and motivate a person to improve sports skills. Action recognition and classification can also be used to produce matching or practice output statistics automatically. The proposed LSTM-BIA utilizes predefined actions by modeling the monitoring effects with discriminative temporal signals. It uses the Spatial pyramid pooling SPP-net to obtain the robust characteristic of each frame's tracked area. The new SPP-net network structure will produce an adjusted description irrespective of the scale and resolution of the object. It could be used for identification and entity recognition and enables variable-length image input into CNN. The experimental results show that the proposed method can evaluate the actual action of sportspersons with high accuracy when compared to other methods.

如今，与运动相关的运动识别在人们的生活中扮演着重要的角色。体育运动中经常研究提及人类运动和手势，以帮助分析、指导和评估活动。运动相关信号的自动检测有助于发现人体的损伤或间接身体问题。体育比赛中具有复杂运动状态和周期性的动作识别模式有助于更准确地估计成功动作状态的持续时间。通过识别剪辑中的活动来识别动作。动作的质量评估根据动作的表现分配定量分数。根据分数，分析动作状态。体育比赛识别的主要问题是正确跟踪运动员的行为。在本文中，已经提出了具有仿生算法 (BIA) 框架的长短期记忆网络 (LSTM) 来识别运动员的动作并激励一个人提高运动技能。动作识别和分类也可用于自动生成匹配或练习输出统计数据。提议的 LSTM-BIA 通过使用判别时间信号对监控效果进行建模来利用预定义的动作。它使用空间金字塔池化 SPP-net 来获得每个帧跟踪区域的鲁棒特性。新的 SPP-net 网络结构将产生调整后的描述，而不管对象的规模和分辨率。它可用于身份识别和实​​体识别，并使可变长度的图像输入到 CNN 中。