Real-Time Continuous Gesture Recognition with Wireless Wearable IMU Sensors

• Published in: 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) pp. 1 - 6
• Main Authors: Wang, Yong-Ting, Ma, Hsi-Pin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2018
• Subjects: Accelerometers; DTW; Feature extraction; Gesture recognition; Gyroscopes; inertial sensors; LDA; PCA; Principal component analysis; Sensors; Support vector machines; SVM
• DOI: 10.1109/HealthCom.2018.8531095

In this paper, we proposed a gesture recognition system with wearable IMU sensors with six axes data (including the accelerometer and gyroscope). The sensor is worn on the wrist, and the raw data are transmitted to the computer via Bluetooth low energy (BLE) to verify the captured data, a recognition system with machine learning classification process is built. The gestures to be recognized can be divided into two categories, with the first being single gestures, which includes ten basic movements, and the second the continuous combinational gestures, which is composed of the previous ten basic movements through different combinations. In order to achieve higher recognition accuracy, we used machine learning process in the system and two analyses, principal component analysis (PCA) and linear discriminant analysis (LDA), to extract well distinguished features. The main advantage of PCA and LDA is reducing dimensions of data while preserving as much of the class discriminatory information as possible. In addition, later processing time can be decreased due to reduced dimensions of data. The experiment is then proceeded with support vector machine (SVM) and dynamic time warping (DTW). With SVM technique, we can recognize movement with higher accuracy and less computation time. High dimension data are also supported. Even non-linear relations can be modeled with more precise classification due to SVM kernels. Dynamic time warping increases recognition accuracy by categorizing movements through the measurement of the resemblance among several temporal sequences which may alter in speed. In the experiment, we can get the accuracy of recognition at 100% for 10 classes with 40 subjects in single gesture under the case of user-dependent. And in continuous combinational gesture for the user-independent case, we can get the accuracy of recognition at 86.99% in fixed combinational gesture, and 60% in arbitrary combinational gesture.