## 8 Conclusions  
This paper has presented the development of an interface to control an assistive technology efficiently and intuitively. The goal was to help people living with upper or lower limb disabilities in their daily tasks. More specifically, the objective was to develop an interface to easily control high-dimensionality ATs with low-dimensionality devices. To do so, a second application of the sequence matching algorithm, presented in [18], has been developed. The main advantage of the SMA is to navigate quickly among different modes of an AT (without having to cluster them into multiple sub-groups) while maintaining a robust system. The idea of the SMA was to combine simple actions into a sequence similar to Morse code. For this project, the SMA has been applied to hand gestures recognized from IMU signals. This brought the problem of having to train a model to recognize those gestures with a dataset of a size significantly lower than that of a typical machine learning implementation. To answer the problem, four lightweight, yet robust, classification algorithms have been identified and tested against a five-class dataset. The classifiers were support vector machines, linear discriminant analysis, adaptative boosting, and K-nearest neighbors. The best performing classifier was the linear SVM, with 95% of the predictions correct on the offline data. Since the size of the training dataset was small, this score could be increased by simply having fewer classes (different gestures) or having gestures that are qualitatively more different. Fortunately, this can be done, since it is the principal advantage of the SMA to give many different instructions with limited physical inputs. The SVM algorithm was preceded by the extraction of the features on the raw accelerometer and gyroscope signals, as well as by a PCA to reduce the dimensionality of the features. These components established the classification pipeline that predicts gestures from inputted IMU signals. The  
Figure 13: Post experiment questionnaire results. pipeline had then been wrapped in a triggering algorithm and implemented in real-time, so that it would output a prediction quickly on any valid input. In addition to the elaboration of the gesture recognition routine, a complete, actual application integrating the IMU, the SMA interface, and a controlled device has been developed. The architecture of the system consisted of these three components (sensor, interface, and device), implemented as independent modules and organized together with a local server as a central piece. In the development environment, this configuration has proven versatile and efficient. The sensor module was a wireless commercial IMU and the device was a computer's mouse and keyboard (moves and shortcuts). Additionally, the user could interact with the system with a GUI to edit settings, gestures, sequences, and mappings, as well as to have visual feedback of the state of the actions performed. The implementation has been tested in two scenarios. For the control of the computer mouse, when the hand gesture interface was compared against a head movement-based interface using a webcam, the latter seems superior. However, when combined, the hand gestures help participants increase their performance by 77%, by offering quick shortcuts accessible with the gestures' sequences. Besides, even if the head movement interface offers better performance for the first half of the experiments, both interfaces may be better adapted to different populations and different scenarios. Although the focus here is on an application of SMA with hand gestures, the interface is not limited to hands or to a computer as an AT. It could use head gestures (tilting) or lower limb moves (leg raise and foot stamps), as well. The application would not require any modifications. If the user wanted to control a robotic arm, instead of a computer, only the controlled-device module (Section 5.1.3) would need to be modified in the system.