Individuals suffering from a progressive neurodegenerative disease, such as amyotrophic lateral sclerosis (ALS), will lose muscle function over time and become completely paralysed. For some time, people with ALS may retain functional tongue movement, despite losing mobility below the neck. These individuals can benefit from using an inductive tongue control interface (ITCI) to control computers or assistive robotic devices to gain independence in their daily lives. Eventually, when the individual can no longer use their tongue, they can rely on a brain computer interface (BCI). However, these require a lot of data to calibrate and function properly. Recording this data while the individual can still use the ITCI can potentially speed up the training process, allowing for an easier transition between interface technologies. This study investigates whether it is possible to create a background data collector for a BCI based on attempted tongue movement by analyzing brain patterns of two individuals with ALS while using an ITCI. The participants used an inductive ITCI in simple cued movement trials while electroencephalogram (EEG) was collected from the motor cortex. The EEG signal indicated that movement-related cortical potentials (MRCP) were generated after the cued movements. After synchronising the signal to the activations recorded on the ITCI, the MRCP became even more apparent. Therefore, it is concluded that it is possible to record MRCPs from individuals with ALS performing tongue movements, that the ITCI can assist in better extracting synchronized MRCP epochs, and that a background data collector for a tongue movement intention-based BCI is very feasible.

Upper limb exoskeletons (ULEs) are robotic devices that can mobilize a severely disabled individual's arm and help the user perform some activities of daily living independently. Despite advancement in the mechanical design of ULEs, a versatile control interface that enables full and continuous control of a ULE with multiple degrees of freedom for a user with disability in both arms and legs (tetraplegia) still requires further research and development. In this study, ten individuals with tetraplegia used a tongue-based interface to fully control a five degrees of freedom ULE for a drinking and a snacking task. This required moving the ULE hand from a wheelchair armrest position to grasp an object (bottle or strawberry) placed on a table in front of the participant, moving the object towards them until it touched a face shield, and placing the object on the table. All participants successfully controlled the exoskeleton and completed the tasks. The drinking task lasted 149.6 s on the first day and 122.9 s (median) on the second day of using the exoskeleton. The participants performed the snacking task only on the first day of ULE use and achieved a median task time of 167.0 s. The study showed that the tongue interface could provide effective, efficient, and safe control of the exoskeleton.

Upper-limb exoskeletons (ULEs) have the potential to assist severely paralyzed individuals. Many rehabilitative upper-limb exoskeletons have been developed in the recent years, where the prominent issue of bulkiness and actuator placement has been effectively resolved by the cable-transmission systems, allowing the actuators to be mounted away from the joints. This results in increasing the compactness and wearability of the exoskeleton. Thus, this article summarizes twenty-five cable-driven upper-limb exoskeletons designed for rehabilitating disabled individuals. This paper also presents a comparison based on usability of ULEs by end users considering the design characteristics and user-based experiments for real-time applications of the arm exoskeletons.

While assistive robotic devices can improve the quality of life for individuals with tetraplegia, it is difficult to provide a high-performing interface that can be fully utilized, with little to no motor functionality. While a brain-computer interface (BCI) can be used with little to no motor functionality, it typically has a low performance. Steady-state visually evoked potentials (SSVEP) provide some of the best performing signals for a BCI, but are rarely investigated for online asynchronous control where not only accuracy is important, but also the computational costs. This study investigates and compares three classifiers: the well-known and high-performing task-related component analysis (TRCA), the computational efficient Spatiotemporal beamformer (STBF) build on the stimulus-locked inter-trace correlation (SLIC) algorithm and our proposed novel algorithm which combines the two: the SLIC-TRCA. Results show the SLIC-TRCA achieving higher accuracies${(95.00\pm 5.36\%}$with a 1s classification window) compared to the TRCA${(88.25\pm 14.58\%)}$and similar compared to the STBF${(91.00\pm 11.02\%)}$while having a much lower computational cost (519% faster than the TRCA and 144% faster than the STBF). We, therefore, believe this algorithm has an exciting potential as it will allow a high classification accuracy without requiring a high-performing CPU.

Tongue-computer interfaces have shown the potential to control assistive devices developed for individuals with severe disabilities. However, current efficient tongue-computer interfaces require invasive methods for attaching the sensor activation units to the tongue, such as piercing. In this study, we propose a noninvasive tongue-computer interface to avoid the requirement of invasive activation unit attachment methods. We developed the noninvasive tongue-computer interface by integrating an activation unit on a frame, and mounting the frame on an inductive tongue-computer interface (ITCI). Thus, the users are able to activate the inductive sensors on the interface by positioning the activation unit with their tongue. They also do not need to remount the activation unit before each use. We performed pointing tests for controlling a computer cursor and number typing tests with two able-bodied participants, where one of them was experienced with using invasive tongue-computer interfaces and other one had no experience. We measured throughput and movement error for pointing tasks, and speed and accuracy for number typing tasks for the evaluation of the feasibility and performance of the developed noninvasive system. Results show that the inexperienced participant achieved similar results with the developed noninvasive tongue-computer interface compared to the current invasive version of the ITCI, while the experienced participant performed better with the invasive tongue-computer interface.

Individuals with tetraplegia are severely limited in terms of independence, which can lead to depression and premature death. The field of robotics has sought to create solutions for increased independence by enabling individuals with tetraplegia to perform physical activities. This includes semiautomatic solutions. The use case of an assistive robotic manipulator (ARM) and thus increased independence could be expanded by allowing remote control, e.g. when the user is lying in bed. This study presents and evaluates semi-autonomous intraoral tongue-based control of a seven degree of freedom ARM/gripper in a near and remote setting. The system consists of a tongue control interface (iTongue), a semi-automatic framework based on YOLOv5 and adaptive levels of automation, an Intel Realsense D435i camera and a JACO robotic manipulator. Two study participants completed ten rounds of controlling the JACO ARM to reach and pick up a soda bottle and pour from it in a cup. The semi-automation improved the grasping performance for both of the study participants, particularly when controlling in a remote setting, decreasing the cognitive load and the overall task completion time. The presented system has the potential to increase the independence and quality of life for individuals with tetraplegia, by enabling the user to perform physical activities even when lying in bed. Future work should include expanding the semi-automation to other activities of daily living and evaluating the system in a greater population and by individuals with tetraplegia.

Complete tetraplegia causes paralysis from the neck down, resulting in a need for constant care and lack of doing activities of daily living by yourself. Exoskeletons have enabled some rehabilitation and movement of paralyzed limbs but are often prohibitively large and heavy. This project aimed to redesign the wrist of the tongue controlled EXOTIC exoskeleton, making it compact and light. The new design used tendon-based technology, removing the motor from the forearm as well as employing 3D print. A proof-of-concept for the new design was obtained through a case study with three subjects.

Aim: To detect and classify tongue movements from single trial electroencephalography (EEG), so that it can be used as a reliable control signal in a brain computer interface (BCI). Method: Thirteen subjects, all BCI-naive, performed four different tongue movements (up, down, left and right), which was detected against an idle state using a common spatial pattern filter with a linear discriminant analysis classifier. Furthermore, the movement types were classified in a one-versus all classification scheme. Results: On average, 72-76% of the movements were detected correctly against the idle state. When all movement types were pooled and detected against the idle state, an accuracy of 80% was obtained. A closer investigation showed that the system correctly detected up to 83% of the executed movements, but had a false positive rate of 13%. The movements were classified with an accuracy of 43%. This was increased to 55% when only left, right and up movements were considered. When only left and right movements where considered they were classified with an average accuracy of 71%. Conclusion: Decoding of tongue movements from the EEG can be used as a reliable control state switch in a BCI and is possible to classify the different movements above chance level. Significance: Residual tongue movements, which is not lost after a spinal cord injury, can be used as a reliable control state switch and it is possibly to detect at least four different movement types.

People with complete tetraplegia who are paralyzed from the neck down require alternative computer and robot interfaces based on the remaining functionalities. The inductive tongue-computer interface (ITCI) consists of two small touchpads that are manipulated by the tongue and can provide individuals with tetraplegia the possibility to control computers, robots, and other electronic devices. The tongue gesture can be used to expand the vocabulary of the ITCI and provide more control commands. In this study for the first time, a tongue gesture recognition method was introduced that can identify a set of six gestures with 94.3% accuracy and a set of 23 gestures with 72.3% accuracy. Furthermore, a robot interface was developed using the tongue gestures and it was compared with a previously tested method based on soft buttons on the ITCI. Four able-bodied participants performed a pouring water task with the two control methods when visual feedback from the interface was presented on a screen and then without the visual feedback. Task completion time for the gesture-based control and the button-based control did not differ. However, gesture-based control provides more control commands that can be used for interfacing other systems.

Intraoral target (typing) and on-screen target (pointing/tracking) selection tasks were performed by 10 participants during 3 consecutive day sessions. Tasks were performed using 2 different intraoral sensor layouts. Reduction of undesired sensor activations while speaking as well as the influence of intraoral temperature variation on the signals of the intraoral interface was investigated. Results showed that intraoral target selection tasks were performed better when the respective sensor was located in the anterior area of the palate, reaching 78 and 16 activations per minute for repetitive and “unordered” sequences, respectively. Virtual target pointing and tracking tasks, of circles of 50, 70, and 100 pixels diameter, showed no significant difference in performance, reaching average pointing throughputs of 0.62 to 0.72 bits per second and relative time on target of 34% to 60%. Speaking tasks caused an average of 10 to 31 involuntary activations per minute in the anterior part of the palate. Intraoral temperature variation between 11.87 °C and 51.37 °C affected the sensor signal baseline in a range from –25.34% to 48.31%. Results from this study provide key design considerations to further increase the efficiency of tongue–computer interfaces for individuals with upper-limb mobility impairments.

An online survey in the form of a questionnaire was conducted to obtain the opinion of computer users with tetraplegia on their current computer interfaces and to assess desirable applications for future independent control using assistive devices. The survey included questions related to information about the respondents' injury/disease, everyday activities and social life, electronic devices and computer programs, evaluation of computer interfaces, and desirable applications for assistive devices. The survey was distributed via tetraplegia associations, magazines, and Internet forums mainly in Denmark and Sweden, but also through other European and American associations. Thirty-one completed questionnaires were collected from individuals with spinal cord injury and other neuromuscular diseases that resulted in tetraplegia. Respondents evaluated gaze and head trackers; speech recognition systems; chin, mouth, and hand joysticks; sip and puff interfaces; and typing sticks. Most interfaces were evaluated in a range from neutral to good. Users expressed a desire for applications to independently control wheelchairs, television sets, doors, and windows. This is, as far as is known, the first study that compares a wide range of current commercial computer interfaces that have been used as part of the users' everyday lives. The answers are useful for designing and developing alternative computer interfaces and assistive devices and for computer-interface users to identify a computer-interface fitting more to his or her needs.