This work presents the development and implementation of a unified multi-sensor human motion capture and gesture recognition system that can distinguish between and classify six different gestures. Data was collected from eleven participants using a subset of five wireless motion sensors (inertial measurement units) attached to their arms and upper body from a complete motion capture system. We compare Support Vector Machines and Artificial Neural Networks on the same dataset under two different scenarios and evaluate the results. Our study indicates that near perfect classification accuracies are achievable for small gestures and that the speed of classification is sufficient to allow interactivity. However, such accuracies are more difficult to obtain when a participant does not participate in training, indicating that more work needs to be done in this area to create a system that can be used by the general population.

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.iii