Wireless intraoral tongue control of an assistive robotic arm for individuals with tetraplegia

Struijk, Lotte N. S. Andreasen; Egsgaard, Line Lindhardt; Lontis, Romulus; Gaihede, Michael; Bentsen, Bo

doi:10.1186/s12984-017-0330-2

Cited by 54 publications

(47 citation statements)

References 21 publications

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“…Interfaces, which use the remaining capabilities of tetraplegics to generate signals suitable for proportional real-time control, may be based on neural activity [8,9], electromyography (EMG) [10,11], gaze [12], tongue movement [13], or head motion [14,15]. While all these solutions are generally feasible to enable robotic arm control, they might not be accessible to a subset of tetraplegic MS patients, due to the presence of interfering symptoms.…”

Section: Direct Control Interfaces For Tetraplegic Ms Patientsmentioning

confidence: 99%

AMiCUS 2.0—System Presentation and Demonstration of Adaptability to Personal Needs by the Example of an Individual with Progressed Multiple Sclerosis

Rudigkeit¹,

Gebhard²

2020

Sensors

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AMiCUS is a human–robot interface that enables tetraplegics to control an assistive robotic arm in real-time using only head motion, allowing them to perform simple manipulation tasks independently. The interface may be used as a standalone system or to provide direct control as part of a semi-autonomous system. Within this work, we present our new gesture-free prototype AMiCUS 2.0, which has been designed with special attention to accessibility and ergonomics. As such, AMiCUS 2.0 addresses the needs of tetraplegics with additional impairments that may come along with multiple sclerosis. In an experimental setup, both AMiCUS 1.0 and 2.0 are compared with each other, showing higher accessibility and usability for AMiCUS 2.0. Moreover, in an activity of daily living, a proof-of-concept is provided that an individual with progressed multiple sclerosis is able to operate the robotic arm in a temporal and functional scope, as would be necessary to perform direct control tasks for use in a commercial semi-autonomous system. The results indicate that AMiCUS 2.0 makes an important step towards closing the gaps of assistive technology, being accessible to those who rely on such technology the most.

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Section: Direct Control Interfaces For Tetraplegic Ms Patientsmentioning

confidence: 99%

AMiCUS 2.0—System Presentation and Demonstration of Adaptability to Personal Needs by the Example of an Individual with Progressed Multiple Sclerosis

Rudigkeit¹,

Gebhard²

2020

Sensors

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“…If the technology is not acceptable for the user, it will not be used. Hence, our design of an exo arm is based on interdisciplinary research into how to advance interaction techniques with tongue control and computer vision [2], how to create flexible and lightweight exos [20] and control systems [47,48], and how to integrate user requirements into the design of the exo arm.…”

Section: The Casementioning

confidence: 99%

Designing a game to explore human artefact ecologies for assistive robotics

Kobbelgaard

Bødker

Kanstrup

2020

Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society

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This paper concerns the creation of design games made to explore and uncover the daily lives and artefact ecologies of users living with paralysis from the neck down. The paper reviews previous notable literature on making design games and presents a novel approach for basing a design game on an activity theoretical framework named the Human Artefact Model. After discussing the creation of the games, the paper reflects on the results and experiences attained whilst using the design games to gather data in the wild. Last, this paper proposes several points of attention to reflect upon when creating design games based on formal theories. CCS CONCEPTS • Human-centred computing → Interaction design; Interaction design process and methods; Participatory design.

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“…Assistive technologies aim to increase quality of life [3–6], reduce dependence on care giver [7] and reduce dependence on the long term care system [8]. Several studies have demonstrated the effectiveness in the use of environment control interfaces (ECI) for environment control or communication through voice commands [9], scan interfaces based on grid structure, eye tracking [10–12] or brain-computer interface (BCI) based on P300 [13], among others.…”

Section: Introductionmentioning

confidence: 99%

User activity recognition system to improve the performance of environmental control interfaces: a pilot study with patients

Bertomeu-Motos

Ezquerro

Barios

et al. 2019

J NeuroEngineering Rehabil

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BackgroundAssistive technologies aim to increase quality of life, reduce dependence on care giver and on the long term care system. Several studies have demonstrated the effectiveness in the use of assistive technology for environment control and communication systems. The progress of brain-computer interfaces (BCI) research together with exoskeleton enable a person with motor impairment to interact with new elements in the environment. This paper aims to evaluate the environment control interface (ECI) developed under the AIDE project conditions, a multimodal interface able to analyze and extract relevant information from the environments as well as from the identification of residual abilities, behaviors, and intentions of the user.MethodsThis study evaluated the ECI in a simulated scenario using a two screen layout: one with the ECI and the other with a simulated home environment, developed for this purpose. The sensorimotor rhythms and the horizontal oculoversion, acquired through BCI2000, a multipurpose standard BCI platform, were used to online control the ECI after the user training and system calibration. Eight subjects with different neurological diseases and spinal cord injury participated in this study. The subjects performed simulated activities of daily living (ADLs), i.e. actions in the simulated environment as drink, switch on a lamp or raise the bed head, during ten minutes in two different modes, AIDE mode, using a prediction model, to recognize the user intention facilitating the scan, and Manual mode, without a prediction model.ResultsThe results show that the mean task time spent in the AIDE mode was less than in the Manual, i.e the users were able to perform more tasks in the AIDE mode during the same time. The results showed a statistically significant differences with p<0.001. Regarding the steps, i.e the number of abstraction levels crossed in the ECI to perform an ADL, the users performed one step in the 90% of the tasks using the AIDE mode and three steps, at least, were necessary in the Manual mode. The user’s intention prediction was performed through conditional random fields (CRF), with a global accuracy about 87%.ConclusionsThe environment analysis and the identification of the user’s behaviors can be used to predict the user intention opening a new paradigm in the design of the ECIs. Although the developed ECI was tested only in a simulated home environment, it can be easily adapted to a real environment increasing the user independence at home.

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Wireless intraoral tongue control of an assistive robotic arm for individuals with tetraplegia

Cited by 54 publications

References 21 publications

AMiCUS 2.0—System Presentation and Demonstration of Adaptability to Personal Needs by the Example of an Individual with Progressed Multiple Sclerosis

AMiCUS 2.0—System Presentation and Demonstration of Adaptability to Personal Needs by the Example of an Individual with Progressed Multiple Sclerosis

Designing a game to explore human artefact ecologies for assistive robotics

User activity recognition system to improve the performance of environmental control interfaces: a pilot study with patients

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