Quaternion-Based Human Gesture Recognition System Using Multiple Body-Worn Intertial Sensors
Shamir Alavi · 2016
In this study, we designed a multi-sensor gesture recognition system that can classify among six different human gestures.Data was collected from eleven participants using five gyroscopic motion sensors tied to their upper body.A total of 1080 samples were collected, which contain almost 6000 gestures collected within a span of 90 minutes.The data were processed and fed into a multiclass Pattern Classification system to classify the gestures.We trained Support Vector Machines and Artificial Neural Networks on the same dataset under two different scenarios to compare the results.A similar study was performed before using modified Hidden Markov Model but the data was collected using a single sensor.Our study indicates that near perfect classification accuracies are achievable.However, such accuracies are more difficult to obtain when a participant does not participate in training even if the test set does not contain any data from the training set.4.2.7 Parameter Selection and Initial Experiment ....................