Neuro-Rehabilitation Therapy with T-FLEX Ankle Exoskeleton and Serious Games: A Case Study.

Acquired Brain Injury (ABI) causes permanent disabilities, such as foot drop. This condition affects the gait pattern, increasing the metabolic cost and risk of falling. Robotics with serious games has shown promising results in the gait rehabilitation context. This paper aims to analyze the effects of using the T-FLEX exoskeleton with (1) Automated Therapy (AT) and (2) Serious Game Therapy (SGT) in two ABI patients. Each participant completed six assisted sessions for each strategy. Results showed that AT increases the user-robot interaction torque by 10% for the first patient and 70% for the second patient, and SGT decreases by 5% for both patients. This way, SGT required the patient to generate torque to execute the ankle movement, while AT did the opposite, resulting in greater device assistance. In the functional assessment, SGT induced variations greater than 50% for the paretic ankle and knee's range of motion (ROM), indicating a potential for motor recovery. Thus, SGT led to improved ankle control and increased gait speed compared to AT. These findings suggest that SGT may be an effective rehabilitation strategy for ABI-related foot drop patients.

Mirror-Based Robotic Therapy for Ankle Recovery with a Serious Game: A Case Study with a Neurological Patient.

Neuromuscular disorders, such as foot drop, severely affect the locomotor function and walking independence after a brain injury event. Mirror-based robotic therapy (MRT) has been a promising rehabilitation strategy favouring upper limb muscle strength and motor control in the last years. However, there are still no studies validating this technique in lower limb experimental protocols. This paper presents an innovative visual and motor feedback strategy based on serious games and MRT modalities. Thus, a preliminary system validation with a healthy participant is performed. Moreover, the strategy's potential effects were investigated in a neurologic patient's short rehabilitation program. After six sessions, the results of the method favoured active ankle plantarflexion range of motion and muscle activation. Although the patient had a positive adaptation at the end of the game, it is necessary to improve the proposed strategy to enhance the robotic experience in the long term.

Expectations and Perceptions of Healthcare Professionals for Robot Deployment in Hospital Environments During the COVID-19 Pandemic.

Several challenges to guarantee medical care have been exposed during the current COVID-19 pandemic. Although the literature has shown some robotics applications to overcome the potential hazards and risks in hospital environments, the implementation of those developments is limited, and few studies measure the perception and the acceptance of clinicians. This work presents the design and implementation of several perception questionnaires to assess healthcare provider's level of acceptance and education toward robotics for COVID-19 control in clinic scenarios. Specifically, 41 healthcare professionals satisfactorily accomplished the surveys, exhibiting a low level of knowledge about robotics applications in this scenario. Likewise, the surveys revealed that the fear of being replaced by robots remains in the medical community. In the Colombian context, 82.9% of participants indicated a positive perception concerning the development and implementation of robotics in clinic environments. Finally, in general terms, the participants exhibited a positive attitude toward using robots and recommended them to be used in the current panorama.

Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG

Introduction: This work presents the development of a novel robotic knee exoskeleton controlled by motion intention based on sEMG, which uses admittance control to assist people with reduced mobility and improve their locomotion. Clinical research remark that these devices working in constant interaction with the neuromuscular and skeletal human system improves functional compensation and rehabilitation. Hence, the users become an active part of the training/rehabilitation, facilitating their involvement and improving their neural plasticity. For recognition of the lower-limb motion intention and discrimination of knee movements, sEMG from both lower-limb and trunk are used, which implies a new approach to control robotic assistive devices. Methods A control system that includes a stage for human-motion intention recognition (HMIR), based on techniques to classify motion classes related to knee joint were developed. For translation of the user's intention to a desired state for the robotic knee exoskeleton, the system also includes a finite state machine and admittance, velocity and trajectory controllers with a function that allows stopping the movement according to the users intention. Results The proposed HMIR showed an accuracy between 76% to 83% for lower-limb muscles, and 71% to 77% for trunk muscles to classify motor classes of lower-limb movements. Experimental results of the controller showed that the admittance controller proposed here offers knee support in 50% of the gait cycle and assists correctly the motion classes. Conclusion The robotic knee exoskeleton introduced here is an alternative method to empower knee movements using sEMG signals from lower-limb and trunk muscles.

Multi-objective control for cooperative payload transport with rotorcraft UAVs.

A novel kinematic formation controller based on null-space theory is proposed to transport a cable-suspended payload with two rotorcraft UAVs considering collision avoidance, wind perturbations, and properly distribution of the load weight. An accurate 6-DoF nonlinear dynamic model of a helicopter and models for flexible cables and payload are included to test the proposal in a realistic scenario. System stability is demonstrated using Lyapunov theory and several simulation results show the good performance of the approach.

A New Controller for a Smart Walker Based on Human-Robot Formation.

This paper presents the development of a smart walker that uses a formation controller in its displacements. Encoders, a laser range finder and ultrasound are the sensors used in the walker. The control actions are based on the user (human) location, who is the actual formation leader. There is neither a sensor attached to the user's body nor force sensors attached to the arm supports of the walker, and thus, the control algorithm projects the measurements taken from the laser sensor into the user reference and, then, calculates the linear and angular walker's velocity to keep the formation (distance and angle) in relation to the user. An algorithm was developed to detect the user's legs, whose distances from the laser sensor provide the information necessary to the controller. The controller was theoretically analyzed regarding its stability, simulated and validated with real users, showing accurate performance in all experiments. In addition, safety rules are used to check both the user and the device conditions, in order to guarantee that the user will not have any risks when using the smart walker. The applicability of this device is for helping people with lower limb mobility impairments.

Analysis of the use of a robot to improve social skills in children with autism spectrum disorder

Abstract Introduction Autism Spectrum Disorder is a set of developmental disorders that imply in poor social skills, lack of interest in activities and interaction with people. Treatments rely on teaching social skills and in such therapies robotics may offer aid. This work is a pilot study, which aims to show the development and usage of a ludic mobile robot for stimulating social skills in ASD children. Methods A mobile robot with a special costume and a monitor to display multimedia contents was designed to interact with ASD children. A mediator controls the robot’s movements in a room prepared for interactive sessions. Sessions are recorded to assess the following social skills: eye gazing, touching the robot and imitating the mediator. The interaction is evaluated using the Goal Attainment Scale and Likert scale. Ten children were evaluated (50% with ASD), using as inclusion criteria children with age 7-8, without use of medication, and without tendency to aggression or stereotyped movements. Results It was observed that the ASD group touched the robot about twice more in average than the control group (CG). They also looked away and imitated the mediator in a quite similar way as the CG, and showed extra social skills (verbal and non-verbal communication). These results are considered an advance in terms of improvement of social skills in ASD children. Conclusions Our studies indicate that the robot stimulated social skills in 4/5 of the ASD children, which shows that its concepts are useful to improve socialization and quality of life.

Towards a new modality-independent interface for a robotic wheelchair.

This work presents the development of a robotic wheelchair that can be commanded by users in a supervised way or by a fully automatic unsupervised navigation system. It provides flexibility to choose different modalities to command the wheelchair, in addition to be suitable for people with different levels of disabilities. Users can command the wheelchair based on their eye blinks, eye movements, head movements, by sip-and-puff and through brain signals. The wheelchair can also operate like an auto-guided vehicle, following metallic tapes, or in an autonomous way. The system is provided with an easy to use and flexible graphical user interface onboard a personal digital assistant, which is used to allow users to choose commands to be sent to the robotic wheelchair. Several experiments were carried out with people with disabilities, and the results validate the developed system as an assistive tool for people with distinct levels of disability.

An inexpensive method for kinematic calibration of a parallel robot by using one hand-held camera as main sensor.

This paper presents a novel method for the calibration of a parallel robot, which allows a more accurate configuration instead of a configuration based on nominal parameters. It is used, as the main sensor with one camera installed in the robot hand that determines the relative position of the robot with respect to a spherical object fixed in the working area of the robot. The positions of the end effector are related to the incremental positions of resolvers of the robot motors. A kinematic model of the robot is used to find a new group of parameters, which minimizes errors in the kinematic equations. Additionally, properties of the spherical object and intrinsic camera parameters are utilized to model the projection of the object in the image and thereby improve spatial measurements. Finally, several working tests, static and tracking tests are executed in order to verify how the robotic system behaviour improves by using calibrated parameters against nominal parameters. In order to emphasize that, this proposed new method uses neither external nor expensive sensor. That is why new robots are useful in teaching and research activities.

Ultra wide-band localization and SLAM: a comparative study for mobile robot navigation.

In this work, a comparative study between an Ultra Wide-Band (UWB) localization system and a Simultaneous Localization and Mapping (SLAM) algorithm is presented. Due to its high bandwidth and short pulses length, UWB potentially allows great accuracy in range measurements based on Time of Arrival (TOA) estimation. SLAM algorithms recursively estimates the map of an environment and the pose (position and orientation) of a mobile robot within that environment. The comparative study presented here involves the performance analysis of implementing in parallel an UWB localization based system and a SLAM algorithm on a mobile robot navigating within an environment. Real time results as well as error analysis are also shown in this work.

Analysis of different feature selection criteria based on a covariance convergence perspective for a SLAM algorithm.

This paper introduces several non-arbitrary feature selection techniques for a Simultaneous Localization and Mapping (SLAM) algorithm. The feature selection criteria are based on the determination of the most significant features from a SLAM convergence perspective. The SLAM algorithm implemented in this work is a sequential EKF (Extended Kalman filter) SLAM. The feature selection criteria are applied on the correction stage of the SLAM algorithm, restricting it to correct the SLAM algorithm with the most significant features. This restriction also causes a decrement in the processing time of the SLAM. Several experiments with a mobile robot are shown in this work. The experiments concern the map reconstruction and a comparison between the different proposed techniques performance. The experiments were carried out at an outdoor environment composed by trees, although the results shown herein are not restricted to a special type of features.

Autonomous assistance navigation for robotic wheelchairs in confined spaces.

In this work, a visual interface for the assistance of a robotic wheelchair's navigation is presented. The visual interface is developed for the navigation in confined spaces such as narrows corridors or corridor-ends. The interface performs two navigation modus: non-autonomous and autonomous. The non-autonomous driving of the robotic wheelchair is made by means of a hand-joystick. The joystick directs the motion of the vehicle within the environment. The autonomous driving is performed when the user of the wheelchair has to turn (90, 90 or 180 degrees) within the environment. The turning strategy is performed by a maneuverability algorithm compatible with the kinematics of the wheelchair and by the SLAM (Simultaneous Localization and Mapping) algorithm. The SLAM algorithm provides the interface with the information concerning the environment disposition and the pose -position and orientation-of the wheelchair within the environment. Experimental and statistical results of the interface are also shown in this work.

SLAM algorithm applied to robotics assistance for navigation in unknown environments.

BACKGROUND: The combination of robotic tools with assistance technology determines a slightly explored area of applications and advantages for disability or elder people in their daily tasks. Autonomous motorized wheelchair navigation inside an environment, behaviour based control of orthopaedic arms or user's preference learning from a friendly interface are some examples of this new field. In this paper, a Simultaneous Localization and Mapping (SLAM) algorithm is implemented to allow the environmental learning by a mobile robot while its navigation is governed by electromyographic signals. The entire system is part autonomous and part user-decision dependent (semi-autonomous). The environmental learning executed by the SLAM algorithm and the low level behaviour-based reactions of the mobile robot are robotic autonomous tasks, whereas the mobile robot navigation inside an environment is commanded by a Muscle-Computer Interface (MCI). METHODS: In this paper, a sequential Extended Kalman Filter (EKF) feature-based SLAM algorithm is implemented. The features correspond to lines and corners -concave and convex- of the environment. From the SLAM architecture, a global metric map of the environment is derived. The electromyographic signals that command the robot's movements can be adapted to the patient's disabilities. For mobile robot navigation purposes, five commands were obtained from the MCI: turn to the left, turn to the right, stop, start and exit. A kinematic controller to control the mobile robot was implemented. A low level behavior strategy was also implemented to avoid robot's collisions with the environment and moving agents. RESULTS: The entire system was tested in a population of seven volunteers: three elder, two below-elbow amputees and two young normally limbed patients. The experiments were performed within a closed low dynamic environment. Subjects took an average time of 35 minutes to navigate the environment and to learn how to use the MCI. The SLAM results have shown a consistent reconstruction of the environment. The obtained map was stored inside the Muscle-Computer Interface. CONCLUSIONS: The integration of a highly demanding processing algorithm (SLAM) with a MCI and the communication between both in real time have shown to be consistent and successful. The metric map generated by the mobile robot would allow possible future autonomous navigation without direct control of the user, whose function could be relegated to choose robot destinations. Also, the mobile robot shares the same kinematic model of a motorized wheelchair. This advantage can be exploited for wheelchair autonomous navigation.

Human-machine interfaces based on EMG and EEG applied to robotic systems.

BACKGROUND: Two different Human-Machine Interfaces (HMIs) were developed, both based on electro-biological signals. One is based on the EMG signal and the other is based on the EEG signal. Two major features of such interfaces are their relatively simple data acquisition and processing systems, which need just a few hardware and software resources, so that they are, computationally and financially speaking, low cost solutions. Both interfaces were applied to robotic systems, and their performances are analyzed here. The EMG-based HMI was tested in a mobile robot, while the EEG-based HMI was tested in a mobile robot and a robotic manipulator as well. RESULTS: Experiments using the EMG-based HMI were carried out by eight individuals, who were asked to accomplish ten eye blinks with each eye, in order to test the eye blink detection algorithm. An average rightness rate of about 95% reached by individuals with the ability to blink both eyes allowed to conclude that the system could be used to command devices. Experiments with EEG consisted of inviting 25 people (some of them had suffered cases of meningitis and epilepsy) to test the system. All of them managed to deal with the HMI in only one training session. Most of them learnt how to use such HMI in less than 15 minutes. The minimum and maximum training times observed were 3 and 50 minutes, respectively. CONCLUSION: Such works are the initial parts of a system to help people with neuromotor diseases, including those with severe dysfunctions. The next steps are to convert a commercial wheelchair in an autonomous mobile vehicle; to implement the HMI onboard the autonomous wheelchair thus obtained to assist people with motor diseases, and to explore the potentiality of EEG signals, making the EEG-based HMI more robust and faster, aiming at using it to help individuals with severe motor dysfunctions.

A new mobile robot control approach via fusion of control signals.

This paper proposes an alternative approach to address the problem of coordinating behaviors in mobile robot navigation: fusion of control signals. Such approach is based on a set of two decentralized information filters, which accomplish the data fusion involved. Besides these two fusion engines, control architectures designed according to this approach also embed a set of different controllers that generate reference signals for the robot linear and angular speeds. Such signals are delivered to the two decentralized information filters, which estimate suitable overall reference signals for the robot linear and angular speeds, respectively. Thus, the background for designing such control architectures is provided by the nonlinear systems theory, which makes this approach different from any other yet proposed. This background also allows checking control architectures designed according to the proposed approach for stability. Such analysis is carried out in the paper, and shows that the robot always reaches its final destination, in spite of either obstacles along its path or the environment layout. As an example, a control architecture is designed to guide a mobile robot in an experiment, whose results allows checking the good performance of the control architecture and validating the design approach proposed as well.