Control Of Robotic Wheelchair Research Articles

Compressive sensing applied to SSVEP-based brain–computer interface in the cloud for online control of a virtual wheelchair

People with severe motor disabilities have great challenges in achieving independence, particularly driving or controlling manually robotic wheelchairs. Existing assistive technologies for robotic wheelchair control often fail to address user needs adequately, becoming necessary an appropriate evaluation of usability and user’s satisfaction for these systems. In fact, user experience and satisfaction are crucial in determining effectiveness, usability and acceptance of these technologies. The aim is to enhance the quality of life of individuals with severe motor disabilities, increasing their independence to perform some daily activities, such as locomotion. This study proposes a cloud Steady-State Visual Evoked Potential (SSVEP)-based Brain–Computer Interface (BCI) system to evaluate its performance through the user experience in commanding a virtual electric-powered wheelchair in a training simulator. Moreover, this study compares traditional wheelchair command modes, such as input interfaces using JoyStick (JS) and Eye Tracker (ET), through standardized questionnaires to measure motivation, ease of use, workload, discomfort, sense of presence and other relevant parameters. The analysis of user experience reveals high levels of motivation, enjoyment, and usability when using the cloud SSVEP-based BCI proposed here. Although our BCI demands higher mental work and discomforts than both ET and JS, its potential for improving the user’s performance is demonstrated here. The findings of this research provide valuable insights for researchers and practitioners in the field of assistive technologies, enabling the development of more personalized systems based on the user’s experience. The main contributions of this study lie in a cloud BCI approach and a comprehensive evaluation methodology to evaluate the user’s experience.

SmartRolling: A human–machine interface for wheelchair control using EEG and smart sensing techniques

Smart wheelchairs based on brain–computer interface (BCI) have been widely utilized recently to address certain mobility problems for people with disability. In this paper, we present SmartRolling, an intuitive human–machine interaction approach for the direct control of robotic wheelchair that jointly leverages EEG signals and motion sensing techniques. Specifically, SmartRolling offers two wheelchair-actuation modes for users with different physical conditions: (1) head motion only — people who are severely disabled but able to do basic tasks using eyes and head, and (2) head and hands motion — in addition to type 1, people who can use functioning hands/arms for extra tasks. The system issues operation commands by recognizing different EEG patterns elicited by motor execution (ME) tasks including eye blink, jaw clench, and fist open/close, while at the same time estimates users’ steering intentions based on their facing direction by leveraging inertial measurements and computer vision techniques. The experiment results demonstrate that the proposed system is robust and effective to meets the individual’s needs and has great potential to promote better health.

Subjective Distance Estimates and Sense of Agency in Robotic Wheelchair Control

Sense of agency (SoA) refers to an individual’s awareness of their own actions. SoA studies seek to find objective indicators for the feeling of agency. These indicators, being related to the feeling of control, have practical application in vehicle design. However, they have not been investigated for actions related to the agent’s body movement inherent to steering a vehicle. In our study, participants operated a robotic wheelchair under three conditions: active control by a participant, direct control by the experimenter and remote control by the experimenter. In each trial, a participant drove the wheelchair until a sound signal occurred, after which they stopped the wheelchair and estimated the travelled distance. The subjective estimates were significantly greater when participants operated the wheelchair by themselves. This result contrasts with observations under static settings in previous studies. In an additional study on the electroencephalographic response to a sound presented at a random time after movement onset, the observed latencies in the N1 component implied that participants might have a higher sense of control when they drove the wheelchair. The proposed methodology might become useful to indirectly assess the degree of operator control of a vehicle, primarily in the field of rehabilitation technologies.

The effect of robotic wheelchair control paradigm and interface on user performance, effort and preference: An experimental assessment

The exact manner in which control is shared between a human and an autonomous system is a crucial factor for assistive robots that provide physical support to people with severe motor impairments. There has however been little comparative study between different control-sharing paradigms within the field of assistive robotics. We present a control architecture for a robotic “smart” wheelchair that allows for the seamless interchange and evaluation of any number of control-sharing paradigms, and so facilitates comparative study between them. We present an implementation of four control sharing paradigms, and results from a study that compares all four to each other and teleoperation, and moreover using multiple control interfaces and across multiple sessions. Experimental results suggest that (i) task performance metrics differ with each control interface, (ii) performance increases with increasing autonomy assistance however it is not statistically significantly different between higher levels of autonomy, (iii) metrics related to user effort show a decrease with increasing autonomy, which is more emphasized with more limited control interface and (iv) how much the autonomy is utilized differs greatly between control paradigms, but not control interfaces. Moreover, (v) for almost all performance metrics, there is a consistent performance increase in Session 2 compared to Session 1, and for both control interfaces. Lastly, subjective questionnaires (control paradigm preference and perceived utility) reveal both similarities and differences between SCI and uninjured subjects. No single control paradigm is the clear winner in performance or preference, suggesting that it will be important to offer end-users multiple control options to accommodate their individual needs and preferences.

Control of Robotic Wheel Chair using EMG Signals for Paralysed Persons

Aim: In recent years, physically challenged people faces more constraint in day to-day movements. In this work, an EMG based hands-free control system for a wheelchair is proposed. Methods: Trapezium EMG signals are acquired, analyzed and then processed into a controlled movements (forward, left, right, etc.) using an embedded circuits for the wheelchair. Results: The system allows the patient to choose a smooth independent movement in the control state or non-control state. The movements depend upon the strength of the EMG signals acquired. Conclusion: The entire system consists of EMG electrodes, a microcontroller and a DC motor for the operation of the wheelchair. The control system is incorporated with muscle processing circuits which give an independent and safe movement to the paraplegic patients.

Development of a Human Machine Interface for Control of Robotic Wheelchair and Smart Environment

In this work, we address the problem of integrating a robotic wheelchair into a smart environment. This approach allows people with disabilities to control home appliances of the environment using a Human Computer Interface (HCI) based on different biological signals. The home appliances includes TV, radio, lights/lamp and fan. Three control paradigms using surface Electromyography (sEMG), Electrooculography (EOG) and Electroencephalography (EEG) signals were used. These signals are captured through a biosignal acquisition system. Three sub-paradigms for sEMG/EOG analyzes were defined: moving eyes horizontally (left/right), raising brow and prolonged clench. On the other hand, the navigation of the wheelchair is executed through an Steady-State Visually Evoked Potentials (SSVEP)-BCI. Each stage of our proposed system showed a good performance for most subjects. Therefore, volunteers were recruited to participate of the study and were distributed in two groups (subjects for home appliances and subjects for SSVEP-BCI). The average accuracy for prolonged clench approach was of 95%, the raising brow was 85% and moving eyes achieved 93%. Multivariate Synchronization Index (MSI) was used for feature extraction from EEG signals. The flickering frequencies were 8.0 Hz (top), 11.0 Hz (right), 13.0 Hz (bottom) and 15.0 Hz (left). Results from this approach showed that classification varies in the range of 45-77% among subjects using window length of 1 s.

A Self-Paced Motor Imagery Based Brain-Computer Interface for Robotic Wheelchair Control

This paper presents a simple self-paced motor imagery based brain-computer interface (BCI) to control a robotic wheelchair. An innovative control protocol is proposed to enable a 2-class self-paced BCI for wheelchair control, in which the user makes path planning and fully controls the wheelchair except for the automatic obstacle avoidance based on a laser range finder when necessary. In order for the users to train their motor imagery control online safely and easily, simulated robot navigation in a specially designed environment was developed. This allowed the users to practice motor imagery control with the core self-paced BCI system in a simulated scenario before controlling the wheelchair. The self-paced BCI can then be applied to control a real robotic wheelchair using a protocol similar to that controlling the simulated robot. Our emphasis is on allowing more potential users to use the BCI controlled wheelchair with minimal training; a simple 2-class self paced system is adequate with the novel control protocol, resulting in a better transition from offline training to online control. Experimental results have demonstrated the usefulness of the online practice under the simulated scenario, and the effectiveness of the proposed self-paced BCI for robotic wheelchair control.

Power assist control of robotic wheelchair based on visual feedback

AbstractThis paper describes a method of vision‐based self‐velocity estimation and its feedback system in force/torque sensorless power assisting control of a wheelchair robot. In this method, three‐dimensional information obtained from stereo images is used along with optical flow vectors for self‐velocity estimation in real time. The human force is also estimated by a sensorless reaction force observer, and the assisting force is calculated by using an estimated force and virtual impedance model. In the paper, the force‐based assist function is integrated into a visual feedback motion controller. This approach using vision‐ and force‐based assist control makes it possible to facilitate direct intelligent interactions between human force and environments such as human following assistance, obstacle avoidance assistance, and so on. Such assistance functions are changeable by selection of the weighting matrix used in velocity estimation, which is based on weighted least square solutions obtained from optical flow vectors. The validity of the proposed approach is verified by several experimental results. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 169(2): 21–28, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20887

Power Assist Control of Robotic Wheelchair Based on Visual Feedback

This paper describes a vision based self-velocity estimation and its feedback system under force/torque sensor-less power assisting control of wheelchair robot. In this method, three dimensional information obtained by stereo images, and the optical flow vectors are also used for self-velocity estimation in real-time. The human force is estimated by sensor-less reaction force observer, and the assisting force is calculated by using its estimated force and virtual impedance model. In the paper, the force based assist function is integrated into visual feedback motion controller. This approach using vision and force based assist control makes it possible to facilitate the direct intelligent interactions between human force and environments such as human following assist, obstacle avoidance one and so on. Such assist functions are changeable by the selection of the weighting matrix in the velocity estimation, which is based on weighted least square solutions from optical flow vectors. The validity of the proposed approach is verified by several experimental results.

DEVELOPMENT OF AGENT-BASED AUTONOMOUS ROBOTIC WHEELCHAIR CONTROL SYSTEMS

In this paper, the intelligent and agent-based autonomous robotic wheelchair controller is proposed. The proposed agent-based autonomous robotic wheelchair control system consists of the functions of path planning, navigation and obstacle avoidance. In this work, the A* algorithm is used to develop the path planning module; the fuzzy logic is used to develop the obstacle avoidance and comfortable wheelchair motion control. Especially, the fuzzy fusion method is used to integrate the proposed fuzzy agent model. Finally, several cases of the individual agent execution, fuzzy module fusion, path planning, and wheelchair coordination are discussed, and the simulation results show that the proposed autonomous robotic wheelchair controller achieves the desired objectives. In addition, the agent-based design increases the model integrability and maintability.