## 2 Related Work  
The proposed system consists of an upper limb exoskeleton as it would enable individuals with tetraplegia to regain some of the lost functionality in their arms. The idea of upper limb exoskeletons for users with movement impairments have, in several previous studies, been shown to be useful for both assistive purposes [6; 11] and for rehabilitation [7; 12; 13]. However, a challenging aspect of using an exoskeleton for people with tetraplegia is how to interface with it. A common approach for controlling upper limb exoskeletons is with EMG (electromyography) [7; 14] to detect muscle activity, which is not feasible in case of severe tetraplegia. Other approaches require the user to control the exoskeleton using a joystick, operated by a single finger [6], which is not possible for complete functional tetraplegia either. Others have explored the idea of using eye movements [13] or voice commands [6] to allow individuals with movement impairments to interface with an upper limb exoskeleton. Eye movements or voice commands are plausible options but can be very tedious to use in the long run and are easily susceptible to noise such as accidental eye  
Figure 1: Overview of the proposed system. A semi-autonomous control scheme combines input from a tongue-based interface and a computer vision module to assist a user in controlling an upper limb exoskeleton. movements or nearby sounds. In one study, the majority of the participants preferred other options over the voice-based control [6]. Another possibility is BCI-based control (brain-computer interface), where signals are measured from the brain of the user controlling the upper limb exoskeleton [12]. However, weak points of a BCI-based interface is the low signal-to-noise ratio, the need for substantial calibration, and the low throughput, both in terms of the number of different commands and also how fast one can issue them. This often restricts the use of BCI-based interfaces to rely on predefined movements which are completely automated [15], forcing the user to relinquish control completely for periods of time. A tongue-based interface does not suffer from many of the issues highlighted above. It offers high throughput, both in terms of the number of possible commands and also in terms of how fast they can be issued [16]. Furthermore, several studies have demonstrated that a tongue-based interface can be used by individuals with tetraplegia to control various assistive devices, such as an upper limb exoskeleton [8] or a robotic arm [17]. These considerations have led to the choice of a tongue-based interface. A semi-autonomous control scheme, where parts of the control are automated using computer vision, is included in the system to further enhance the tongue-based control of the exoskeleton. Several studies have reported increased performance when employing computer vision for semi-autonomous control of assistive robotic manipulators [10], such as completing tasks faster [18; 19] or being more precise in the movement of the manipulator [20; 21]. Furthermore, there are several examples of computer vision either improving the fine control of an upper limb exoskeleton [13; 21] or automating entire parts of a task [11; 22; 23] for users with paralysis. Many of the approaches relying on computer vision employ a clear-cut strategy for arbitrating control between the user and the system, where certain parts of the process are completely automated [10]. For example, reaching for and grasping an object once the user triggers this predefined task, either from a tongue-based interface [20], through voice commands [18], or through eye movements [11; 22]. The user would, in these cases, relinquish complete control until the task is completed, i.e., the object is reached and grasped. This fixed level of autonomy, where the automated process is clearly defined, is likely common because it is easy to implement, easy to understand for the user, and it improves performance in many cases.