1,134 research outputs found
Co-adaptive control strategies in assistive Brain-Machine Interfaces
A large number of people with severe motor disabilities cannot access any of the
available control inputs of current assistive products, which typically rely on residual
motor functions. These patients are therefore unable to fully benefit from existent
assistive technologies, including communication interfaces and assistive robotics. In
this context, electroencephalography-based Brain-Machine Interfaces (BMIs) offer a
potential non-invasive solution to exploit a non-muscular channel for communication
and control of assistive robotic devices, such as a wheelchair, a telepresence
robot, or a neuroprosthesis. Still, non-invasive BMIs currently suffer from limitations,
such as lack of precision, robustness and comfort, which prevent their practical
implementation in assistive technologies.
The goal of this PhD research is to produce scientific and technical developments
to advance the state of the art of assistive interfaces and service robotics based on
BMI paradigms. Two main research paths to the design of effective control strategies
were considered in this project. The first one is the design of hybrid systems, based on
the combination of the BMI together with gaze control, which is a long-lasting motor
function in many paralyzed patients. Such approach allows to increase the degrees
of freedom available for the control. The second approach consists in the inclusion
of adaptive techniques into the BMI design. This allows to transform robotic tools and
devices into active assistants able to co-evolve with the user, and learn new rules of
behavior to solve tasks, rather than passively executing external commands.
Following these strategies, the contributions of this work can be categorized
based on the typology of mental signal exploited for the control. These include:
1) the use of active signals for the development and implementation of hybrid eyetracking
and BMI control policies, for both communication and control of robotic
systems; 2) the exploitation of passive mental processes to increase the adaptability
of an autonomous controller to the user\u2019s intention and psychophysiological state,
in a reinforcement learning framework; 3) the integration of brain active and passive
control signals, to achieve adaptation within the BMI architecture at the level of
feature extraction and classification
In-home and remote use of robotic body surrogates by people with profound motor deficits
By controlling robots comparable to the human body, people with profound
motor deficits could potentially perform a variety of physical tasks for
themselves, improving their quality of life. The extent to which this is
achievable has been unclear due to the lack of suitable interfaces by which to
control robotic body surrogates and a dearth of studies involving substantial
numbers of people with profound motor deficits. We developed a novel, web-based
augmented reality interface that enables people with profound motor deficits to
remotely control a PR2 mobile manipulator from Willow Garage, which is a
human-scale, wheeled robot with two arms. We then conducted two studies to
investigate the use of robotic body surrogates. In the first study, 15 novice
users with profound motor deficits from across the United States controlled a
PR2 in Atlanta, GA to perform a modified Action Research Arm Test (ARAT) and a
simulated self-care task. Participants achieved clinically meaningful
improvements on the ARAT and 12 of 15 participants (80%) successfully completed
the simulated self-care task. Participants agreed that the robotic system was
easy to use, was useful, and would provide a meaningful improvement in their
lives. In the second study, one expert user with profound motor deficits had
free use of a PR2 in his home for seven days. He performed a variety of
self-care and household tasks, and also used the robot in novel ways. Taking
both studies together, our results suggest that people with profound motor
deficits can improve their quality of life using robotic body surrogates, and
that they can gain benefit with only low-level robot autonomy and without
invasive interfaces. However, methods to reduce the rate of errors and increase
operational speed merit further investigation.Comment: 43 Pages, 13 Figure
Hybrid brain/neural interface and autonomous vision-guided whole-arm exoskeleton control to perform activities of daily living (ADLs)
[EN] Background
The aging of the population and the progressive increase of life expectancy in developed countries is leading to a high incidence of age-related cerebrovascular diseases, which affect people's motor and cognitive capabilities and might result in the loss of arm and hand functions. Such conditions have a detrimental impact on people's quality of life. Assistive robots have been developed to help people with motor or cognitive disabilities to perform activities of daily living (ADLs) independently. Most of the robotic systems for assisting on ADLs proposed in the state of the art are mainly external manipulators and exoskeletal devices. The main objective of this study is to compare the performance of an hybrid EEG/EOG interface to perform ADLs when the user is controlling an exoskeleton rather than using an external manipulator.
Methods
Ten impaired participants (5 males and 5 females, mean age 52 +/- 16 years) were instructed to use both systems to perform a drinking task and a pouring task comprising multiple subtasks. For each device, two modes of operation were studied: synchronous mode (the user received a visual cue indicating the sub-tasks to be performed at each time) and asynchronous mode (the user started and finished each of the sub-tasks independently). Fluent control was assumed when the time for successful initializations ranged below 3 s and a reliable control in case it remained below 5 s. NASA-TLX questionnaire was used to evaluate the task workload. For the trials involving the use of the exoskeleton, a custom Likert-Scale questionnaire was used to evaluate the user's experience in terms of perceived comfort, safety, and reliability.
Results
All participants were able to control both systems fluently and reliably. However, results suggest better performances of the exoskeleton over the external manipulator (75% successful initializations remain below 3 s in case of the exoskeleton and bellow 5s in case of the external manipulator).
Conclusions
Although the results of our study in terms of fluency and reliability of EEG control suggest better performances of the exoskeleton over the external manipulator, such results cannot be considered conclusive, due to the heterogeneity of the population under test and the relatively limited number of participants.This study was funded by the European Commission under the project AIDE (G.A. no: 645322), Spanish Ministry of Science and Innovation, through the projects PID2019-108310RB-I00 and PLEC2022-009424 and by the Ministry of Universities and European Union, "fnanced by European Union-Next Generation EU" through Margarita Salas grant for the training of young doctors.CatalĂĄn, JM.; Trigili, E.; Nann, M.; Blanco-Ivorra, A.; Lauretti, C.; Cordella, F.; Ivorra, E.... (2023). Hybrid brain/neural interface and autonomous vision-guided whole-arm exoskeleton control to perform activities of daily living (ADLs). Journal of NeuroEngineering and Rehabilitation. 20(1):1-16. https://doi.org/10.1186/s12984-023-01185-w11620
Autonomous Grasping of 3-D Objects by a Vision-Actuated Robot Arm using Brain-Computer Interface
A major drawback of a BrainâComputer Interface-based robotic manipulation is the complex trajectory planning of the robot arm to be carried out by the user for reaching and grasping an object. The present paper proposes an intelligent solution to the existing problem by incorporating a novel Convolutional Neural Network (CNN)-based grasp detection network that enables the robot to reach and grasp the desired object (including overlapping objects) autonomously using a RGB-D camera. This network uses a simultaneous object and grasp detection to affiliate each estimated grasp with its corresponding object. The subject uses motor imagery brain signals to control the pan and tilt angle of a RGB-D camera mounted on a robot link to bring the desired object inside its Field-of-view presented through a display screen while the objects appearing on the screen are selected using the P300 brain pattern. The robot uses inverse kinematics along with the RGB-D camera information to autonomously reach the selected object and the object is grasped using proposed grasping strategy. The overall BCI system outperforms other comparative systems involving manual trajectory planning significantly. The overall accuracy, steady-state error, and settling time of the proposed system are 93.4%, 0.05%, and 15.92 s, respectively. The system also shows a significant reduction of the workload of the operating subjects in comparison to manual trajectory planning based approaches for reaching and grasping
Robot Autonomy for Surgery
Autonomous surgery involves having surgical tasks performed by a robot
operating under its own will, with partial or no human involvement. There are
several important advantages of automation in surgery, which include increasing
precision of care due to sub-millimeter robot control, real-time utilization of
biosignals for interventional care, improvements to surgical efficiency and
execution, and computer-aided guidance under various medical imaging and
sensing modalities. While these methods may displace some tasks of surgical
teams and individual surgeons, they also present new capabilities in
interventions that are too difficult or go beyond the skills of a human. In
this chapter, we provide an overview of robot autonomy in commercial use and in
research, and present some of the challenges faced in developing autonomous
surgical robots
Biosignalâbased humanâmachine interfaces for assistance and rehabilitation : a survey
As a definition, HumanâMachine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignalâbased HMIs for assistance and rehabilitation to outline stateâofâtheâart and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, fullâtext), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An everâgrowing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIsâ complex-ity, so their usefulness should be carefully evaluated for the specific application
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