PE Department of Tongling University, Tongling 244061, Anhui 244061, China.
Comput Intell Neurosci. 2022 Jun 3;2022:9836697. doi: 10.1155/2022/9836697. eCollection 2022.
Fencing is an advantageous event for a country to participate in the Asian Games. The discussion and research on the means and methods of special fencing ability training are of great significance to the further development of fencing in the country. Based on the multisensor information fusion technology, this paper develops a special fencing training system with digital monitoring and intelligent decision-making. The multisensor information fusion technology scheme, which is reported in this paper, is used to perform decision-level fusion based on fuzzy inference technology whereas Kalman filter is used to optimize the original information. On the basis of the overall structure, mechanical mechanism, and performance analysis, the mechanical prototype of the fencing special ability training system was developed whereas design, processing, and debugging of the prototype were completed. Combined with the mechanical prototype design of various index parameter display structures based on multisensor information fusion technology, the digital processing of the fencing special ability training system is realized. The force, speed, position, and other characteristic quantities in the fencing special training system are collected by the pressure and rotational speed sensors respectively whereas USB communication technology is used for data transmission. On the basis of in-depth analysis of the characteristics of the special training system for fencing, an abstract system of pedaling power based on the center of gravity and trend models is designed where the Kalman filter is reserved for the special application fields. Moreover, the core role of Kalman filter is carried out when original data information is needed to be obtained. By constructing a force distribution pressure center trajectory measurement model, a training load and training parameter measurement model, and a training level evaluation model, the data-level raw information is specially processed to generate more training parameters that meet specific characteristics, making the multisensor information fusion technology more efficient. It is well integrated into the fencing special ability training system. By applying fuzzy reasoning technology, the special training parameters and the relationship between these parameters are transformed into fuzzy sets and fuzzy rules, and the perceptual knowledge of sports training experience is transformed into fuzzy rules. With this decision-level data fusion method, the fencing special training system has certain intelligent functions.
击剑是一个国家参加亚运会的优势项目。探讨和研究特殊击剑能力训练的手段和方法，对我国击剑运动的进一步发展具有重要意义。本研究基于多传感器信息融合技术，开发了具有数字监测和智能决策的特殊击剑训练系统。本文报道的多传感器信息融合技术方案，采用基于模糊推理技术的决策级融合，而卡尔曼滤波则用于优化原始信息。在总体结构、机械机理和性能分析的基础上，开发了击剑特殊能力训练系统的机械原型，并完成了原型的设计、加工和调试。结合基于多传感器信息融合技术的各种指标参数显示结构的机械原型设计，实现了击剑特殊能力训练系统的数字处理。压力和转速传感器分别采集击剑特殊训练系统中的力、速度、位置等特征量，采用 USB 通信技术进行数据传输。在深入分析击剑特殊训练系统特点的基础上，设计了基于重心和趋势模型的抽象蹬力系统，为特殊应用领域保留了卡尔曼滤波器。此外，当需要获取原始数据信息时，核心作用由卡尔曼滤波器来执行。通过构建力分布压力中心轨迹测量模型、训练负荷和训练参数测量模型以及训练水平评估模型，对数据级原始信息进行特殊处理，生成更多符合特定特征的训练参数，使多传感器信息融合技术更高效地融入击剑特殊能力训练系统。通过应用模糊推理技术，将特殊训练参数及其关系转化为模糊集和模糊规则，将运动训练经验的感性知识转化为模糊规则。通过这种决策级数据融合方法，击剑特殊训练系统具有一定的智能功能。
