南昌大学机电工程学院
TP312
国家自然科学基金资助项目(61263045),国家自然科学基金项目(面上项目,重点项目,重大项目)
In order to achieve the goal of dumbbell movement classification and recognition,an inertial sensor module is installed on the dumbbell. Through collecting the motion signals during the dumbbell exercise,the eigenvector of five kinds of dumbbell movement,such as flat lifting,counter-grip bending,hammer bending,and curling are extracted after signal standardization,filtering and periodic segmentation based on initial static variables.The improved ReliefF feature selection algorithm is used to select the optimal eigenvector and the support vector machine based on balanced decision tree is used to classify and recognize different dumbbell movements. Through testing on the dumbbell motion recognition system independently developed in the laboratory, the results show that the system can recognize the dumbbell movement within a single dumbbell movement cycle,and the recognition rate can reach more than 90%,which lays the foundation for providing more personalized dumbbell action guidance.
刘国平. 基于改进ReliefF算法的哑铃动作识别[J]. 科学技术与工程, 2019, 19(32): 219-224.
刘国平. Dumbbell motion recognition based on improved ReliefF algorithm[J]. Science Technology and Engineering,2019,19(32):219-224.