Elderly and disabled people can experience considerable difficulties when driving a powered wheelchair, especially if they do not possess the fine steering capacities that are required to perform certain manoeuvres, like avoiding obstacles or docking at tables. In order to help these people, several ''intelligent'' wheelchairs have been developed in the past, meaning that a powered wheelchair was endowed with the abilities to provide navigational assistance to its user. In such a scenario, control over the wheelchair is shared between the user and the intelligent assistance. The problem of the existing intelligent wheelchairs is, however, that the rules for assistance are hard-coded and by consequence not adaptable to the personal handicap and needs of a specific user. Therefore, this paper proposes a new, more user-centered approach to shared wheelchair control. The presented framework executes two tasks: it continuously estimates the user's intention and it determines whether the user needs assistance to achieve that intention. An implicit user model is introduced and incorporated in the framework, in order to make the execution of both tasks adaptable to a specific user. This paper presents the proposed framework, along with experimental results in simulation and on a real wheelchair.

This paper presents an extension of current Global Dynamic Window approaches to arbitrarily shaped holonomic and non-holonomic mobile robots. The algorithm proceeds in two stages. In order to account for an arbitrary robot cross section, the first stage takes the robot’s orientation explicitly into account by constructing a navigation function in the (x, y, θ) configuration space. In a second stage, an admissible velocity is chosen from a window around the robot’s current velocity, which contains all velocities that can be reached under the acceleration constraints. Fast computation over large areas is achieved by adopting multi-resolution (x, y) and (x, y, θ) planning. Several measures are taken to obtain safe and robust robot behaviour. Experimental results on our wheelchair test platform show the feasibility of the approach.

The last years have witnessed a significant increase in the percentage of old and disabled people. Members of this population group very often require extensive help for performing daily tasks like moving around or grasping objects. Unfortunately, assistive technology is not always available to people needing it. For instance, steering a wheelchair can represent an extremely fatiguing or simply impossible task to many elderly or disabled users. Most of the existing assistance platforms try to help users without considering their specific needs. However, driving performance may vary considerably across users due to different pathologies or just due to temporary effects like fatigue. Therefore, we propose in this paper a user adapted shared control approach aimed at helping users in driving a power wheelchair. Adaption to the user is achieved by estimating the user's true intent out of potentially noisy steering signals before assisting him/her. The user's driving performance is explicitly modeled in order to recognize the user's intention or plan together with the uncertainty on it. Safe navigation is achieved by merging the potentially noisy input of the user with fine motion trajectories computed online by a 3D planner. Encouraging results on assisting a user who cannot steer to the left are reported on K.U.Leuven's intelligent wheelchair Sharioto.

Many elderly and physically impaired people experience difficulties when maneuvering a powered wheelchair. In order to ease maneuvering, powered wheelchairs have been equipped with sensors, additional computing power and intelligence by various research groups. This paper presents a Bayesian approach to maneuvering assistance for wheelchair driving, which can be adapted to a specific user. The proposed framework is able to model and estimate even complex user intents, i.e. wheelchair maneuvers that the driver has in mind. Furthermore, it explicitly takes the uncertainty on the user's intent into account. Besides during intent estimation, user-specific properties and uncertainty on the user's intent are incorporated when taking assistive actions, such that assistance is tailored to the user's driving skills. This decision making is modeled as a greedy Partially Observable Markov Decision Process (POMDP). Benefits of this approach are shown using experimental results in simulation and on our wheelchair platform Sharioto.

The use of shared control techniques has a profound impact on the performance of a robotic assistant controlled by human brain signals. However, this shared control usually provides assistance to the user in a constant and identical manner each time. Creating an adaptive level of assistance, thereby complementing the user's capabilities at any moment, would be more appropriate. The better the user can do by himself, the less assistance he receives from the shared control system; and vice versa. In order to do this, we need to be able to detect when and in what way the user needs assistance. An appropriate assisting behaviour would then be activated for the time the user requires help, thereby adapting the level of assistance to the specific situation. This paper presents such a system, helping a brain-computer interface (BCI) subject perform goal-directed navigation of a simulated wheelchair in an adaptive manner. Whenever the subject has more difficulties in driving the wheelchair, more assistance will be given. Experimental results of two subjects show that this adaptive shared control increases the task performance. Also, it shows that a subject with a lower BCI performance has more need for extra assistance in difficult situations, such as manoeuvring in a narrow corridor.

The possibility to act upon the surrounding environment without using our human nervous system’s efferent pathways enables a new interaction modality that can boost and speed up the human sensor-effector loop. In recent years, brain-computer interface (BCI) research is exploring many applications in different fields: communication, environmental control, robotics and mobility, and neuroprosthetics [1] [2] [3] [4] [5] [6] [7]. Our work in the MAIA project is focused on developing asynchronous and non-invasive BCI to control robots and wheelchairs [7] [8]. It means that the users control such devices spontaneously and at their own paced, by learning to voluntary control specific electroencephalogram (EEG) features measured from the scalp. To this end, the users learn how to voluntary modulate different oscillatory rhythms by execution of different mental tasks (motor and cognitive). To facilitate this learning process, machine learning techniques are utilized, both to find those subject-specific EEG features that maximize the separability between the patterns generated by executing the mental tasks [9], and to train classifiers that minimize the classification error rates of these subject-specific patterns [7]. Finally, to assist the control task, different levels of intelligence are implemented in the device jointly with shared control techniques between the two interacting agents, the BCI system and the intelligent device [10] [11]. One of the main challenges of a non-invasive BCI based on spontaneous brain activity is the non-stationary nature of the EEG signals. Shenoy and co-workers [12] describe two sources of non-stationarity, namely differences

Brain-Computer Interfaces (BCIs) need an uninterrupted flow of feedback to the user, which is usually delivered through the visual channel. Our aim is to explore the benefits of vibrotactile feedback during users' training and control of EEG-based BCI applications. An experimental setup for delivery of vibrotactile feedback, including specific hardware and software arrangements, was specified. We compared vibrotactile and visual feedback, addressing the performance in presence of a complex visual task on the same (visual) or different (tactile) sensory channel. The preliminary experimental setup included a simulated BCI control, in which all parts reflected the computational and actuation process of an actual BCI, except the source, which was simulated using a "noisy" PC mouse. Results indicated that the vibrotactile channel can function as a valuable feedback modality with reliability comparable to the classical visual feedback. Advantages of using a vibrotactile feedback emerged when the visual channel was highly loaded by a complex task.

The term brain computer interface (BCI) is used to denote a direct communication pathway between the brain and an external device. In this poster we propose a BCI based on Steady State Visual Evoked Potentials (SSVEP). This BCI system attains the maximum theoretical transfer rates and an empirical transfer rate that surpass largely all the systems based on movement imagination. As additional features, the system false positive rate is negligible and with a minimal attention of the subject. This means that the subject can follow a conversation or divide visual attention without compromising the control of the external device. Expected to work for several classes, current version allow sending 4 different commands. Using this system we perform a telepresence experiment where one subject at our lab explored another lab several miles away by controlling in real time a robot through Internet.

Correspondence should be addressed to Febo Cincotti, f.cincotti@hsantalucia.itReceived 18 February 2007; Accepted 26 June 2007Recommended by Andrzej CichockiTo be correctly mastered, brain-computer interfaces (BCIs) need an uninterrupted flow of feedback to the user. This feedback isusuallydeliveredthroughthevisualchannel.Ouraimwastoexplorethebenefitsofvibrotactilefeedbackduringusers’trainingandcontrol of EEG-based BCI applications. A protocol for delivering vibrotactile feedback, including specific hardware and softwarearrangements, was specified. In three studies with 33 subjects (including 3 with spinal cord injury), we compared vibrotactile andvisual feedback, addressing: (I) the feasibility of subjects’ training to master their EEG rhythms using tactile feedback; (II) thecompatibility of this form of feedback in presence of a visual distracter; (III) the performance in presence of a complex visual taskonthesame(visual)ordifferent (tactile) sensory channel. The stimulation protocol we developed supports a general usage ofthe tactors; preliminary experimentations. All studies indicated that the vibrotactile channel can function as a valuable feedbackmodality with reliability comparable to the classical visual feedback. Advantages of using a vibrotactile feedback emerged whenthe visual channel was highly loaded by a complex task. In all experiments, vibrotactile feedback felt, after some training, morenatural for both controls and SCI users.Copyright © 2007 Febo Cincotti et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

To be correctly mastered, brain-computer interfaces (BCIs) need an uninterrupted flow of feedback to the user. This feedback is usually delivered through the visual channel. Our aim was to explore the benefits of vibrotactile feedback during users' training and control of EEG-based BCI applications. A protocol for delivering vibrotactile feedback, including specific hardware and software arrangements, was specified. In three studies with 33 subjects (including 3 with spinal cord injury), we compared vibrotactile and visual feedback, addressing: (I) the feasibility of subjects' training to master their EEG rhythms using tactile feedback; (II) the compatibility of this form of feedback in presence of a visual distracter; (III) the performance in presence of a complex visual task on the same (visual) or different (tactile) sensory channel. The stimulation protocol we developed supports a general usage of the tactors; preliminary experimentations. All studies indicated that the vibrotactile channel can function as a valuable feedback modality with reliability comparable to the classical visual feedback. Advantages of using a vibrotactile feedback emerged when the visual channel was highly loaded by a complex task. In all experiments, vibrotactile feedback felt, after some training, more natural for both controls and SCI users.

Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased. This paper introduces a shared control system that helps the subject in driving an intelligent wheelchair with a noninvasive brain interface. The subject's steering intentions are estimated from electroencephalogram (EEG) signals and passed through to the shared control system before being sent to the wheelchair motors. Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it. These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.

This paper describes a novel adaptive filter approach to reduce the handicap a patient may experience when navigating an electric wheelchair. The filter automatically adapts to the specific handicap the patient has by training a connectionist structure that converts the joystick signal of the patient to the signal a reference user would give in the same context. Experimental results show that for various handicaps the filter improves the driving performance significantly

Many elderly and disabled people today experience difficulties when manoeuvring an electric wheelchair. In order to help these people, several robotic assistance platforms have been devised in the past. In most cases, these platforms consist of separate assistance modes, and heuristic rules are used to automatically decide which assistance mode should be selected in each time step. As these decision rules are often hard-coded and do not take uncertainty regarding the user's intent into account, assistive actions may lead to confusion or even irritation if the user's actual plans do not correspond to the assistive system's behavior. In contrast to previous approaches, this paper presents a more user-centered approach for recognizing the intent of wheelchair drivers, which explicitly estimates the uncertainty on the user's intent. The paper shows the benefit of estimating this uncertainty using experimental results with our wheelchair platform Sharioto