## 1 Introduction  
Assistive technologies (ATs) are used all around the world by people living with all kinds of disabilities. For instance, the use of a robotic arm has been shown to help people living with upper limb disabilities with their daily tasks [1]. Another study on exoskeletons proved they have good potential for functional mobility in people with spinal cord injury [2]. While the emphasis is often put on the assistive device itself, such systems actually include three important parts: the user, the control interface, and the device (e.g., robotic arm and exoskeleton). In fact, this is the case for any human-machine interaction and more often than not, the interface, which links the device to the user, seems to be the key element in the system [3, 4, 5]. So, when it comes to design, it is worth spending time on developing a valuable interface, especially with ATs, on which the ability of a user to be independent in his or her daily living task may heavily rely on it. Besides, because of the specific abilities and needs being very unique, even for the same diagnosis, the control interface needs to be either tailor-made or flexible and adaptable. Control interfaces are built using various sensors that may be combined with intelligent algorithms. Recently, many interfaces using different technologies have emerged, and they are yet being improved. The simplest interfaces like sip-and-puff or tongue control [6, 7] are widely used in practice, due to the absence of complexity in their implementation and usage. Another popular instance is voice control. It has been shown that commercial voice assistants can help people in their daily tasks by providing functionalities to interact with their phone [8], handle lights, or simply play music [3]. Voice assistants often require an internet connection, but lighter versions can also be implemented offline [9]. Other options include electromyography (EMG) interfaces [10, 11, 12], which use electrical activity in muscles as input signals and control via inertial measurement units (IMUs) [13, 14], which combine accelerometer and gyroscope signals. For instance, IMUs or EMGs can be positioned on a limb that a user can move easily (like the head, arms, or legs) and act as an input mechanism to control a robotic arm or the mouse of a computer. One major and obvious issue to address with almost any interface-AT pair is that many interfaces have limited actual physical commands to efficiently control all of the functions of their corresponding AT. Indeed, controlling a high-dimensionality device with a low-dimensionality sensor (around which the interface is built) can be quite challenging. The dimensionality of components in this context can be interpreted as qualitative metrics to compare the work space of those components, in terms of degrees of freedom (DoF), number of functions, or some other physical restrictions, such as the number of actions a user can perform according to his or her abilities or the number of controls available. For instance, whereas a six-DoF assistive robotic arm may need 20 commands to operate (e.g., forward, backward, up, down, left, fingers, options, etc. ), a computer may need 10 (mouse left, right, up, down, left click, right click, copy, paste, etc.). However, in both cases, the dimensionality of the control interface depends more on the abilities of the users (the actions they can perform and their reach). Therefore, many users only have access to basic devices, such as switches, to control the AT. In any case, a common way to deal with the interface's lower dimensionality is often to arrange the AT's modes in sub-control groups. As shown in Figure 1, with this approach, accessing a mode can require a series of actions to browse through the nested groups [1, 15, 16]. Thus, a strategy needs to be implemented within the interface to either reduce the total number of modes, facilitate switching between them, or somehow artificially augment the dimensionality of the interface itself.