Arm Motion Capture and Recognition Algorithm Based on MEMS Sensor Networks and KPA

With the development of artificial intelligence, human-computer interaction technology has become more and more popular. Motion capture and recognition technology based on sensor networks and machine learning algorithms gradually drawn wide attention and extensive works have been done. This article introduces the arm motion capture system based on MEMS sensor networks that takes the micro inertial measurement unit as the core, and uses the Kernel Perceptron Algorithm (KPA) to realize motion classification and recognition. This algorithm combines the advantages of Support Vector Machine (SVM) and perceptron algorithm to gain a faster model training speed under the premise of high recognition accuracy. Extensive experiments can prove that arm motions can be accurately captured by MEMS sensor networks, and the KPA has a good performance of motion recognition and faster model training speed than SVM.