Gesture spotting from IMU and EMG data for human-robot interaction - Lopes, João Diogo Faria (2016)
Gesture spotting is an important factor in the development of human-machine interaction modalities, which can be improved by reliable motion segmentation methods. This work uses a gesture segmentation method in order to distinguish dynamic from static motions, using IMU and EMG sensor modalities. The performance of the sensors individually as well as their combination was evaluated, with thresholds and window size manually defined for each sensor modality, through 60 sequences performed by 6 users. The method which used the IMU alone obtained the best results in regards to the total segmentation error (11.88%), in comparison to the other two methods (EMG = 43.75% e IMU+EMG= 12.92%). When considering gestures which only contain arm movement, the best error obtained was 1.11% by the IMU method (EMG = 58.89% e IMU+EMG= 7.22%). However, when considering gestures which have only hand motion, the combination of the 2 sensors achieved the best performance, with an error of 10% (IMU = 30.83% e EMG= 17.5%). Results of the sensor fusion modality varied greatly depending on user, with segmentation errors varying between 1.25% and 26.25%, where users with more training obtained better results. Application of different filtering method to the EMG data as a solution to the limb position resulted in an error for the combination of sensors of 9.17%, with all gestures performing similarly or better than the IMU method but with an increased number of non-detected gestures.