Adaptive control of one-DOF portable rehabilitation robot for wrist training

Stroke is one of the leading causes of severe disability. The application of rehabilitation robots is increasing rapidly to help in recovering this disability through rehabilitation training. By using robot, the patient may perform the training more frequently. Various rehabilitation robots have been developed with a set of rehabilitation training programs with different haptic modalities. Different controllers were applied to provide accurate motor control for the rehabilitation robot and PID controller is one of the commonly used controllers. A robot named CR2- Haptic, which is used to train upper limbs, was developed in UTM with a set of rehabilitation training programs with PID controller that was designed for the patients having standard weight and wrist flexibility. The robot is successfully being used for training of stroke patients. One of the limitations for the PID controller is that it is not able to adapt its controller if the load is over its capability, since the robot controller is tuned based on a set standard weight. Therefore, the robot controller was not able to adapt itself to rotate the patient’s hand for patient with high muscle stiffness which is common in stroke patient. Thus, it limits the use of the device to only patient with low muscle spasticity. Whenever the unknown and inaccessible load torque is imposed, the system will have the steady-and/or transientstate error. Therefore, in this project, a model reference adaptive controller (MRAC) which is able to adapt itself based on different patient conditions has been designed using Lyapunov method and implemented on the CR2-Haptic device to reduce the positioning error and make it more beneficial for wide range of stroke patients. The controller has been tested on subjects of different muscles stiffness. It has performed better for accurate positioning of the end effecter for patients with different weight and muscle stiffness. The results show that the designed controller is able to cope with the variations in limb’s stiffness of the patients without the aid of any additional stiffness detection sensors

Down-Conditioning of Soleus Reflex Activity using Mechanical Stimuli and EMG Biofeedback

Spasticity is a common syndrome caused by various brain and neural injuries, which can severely impair walking ability and functional independence. To improve functional independence, conditioning protocols are available aimed at reducing spasticity by facilitating spinal neuroplasticity. This down-conditioning can be performed using different types of stimuli, electrical or mechanical, and reflex activity measures, EMG or impedance, used as biofeedback variable. Still, current results on effectiveness of these conditioning protocols are incomplete, making comparisons difficult. We aimed to show the within-session task- dependent and across-session long-term adaptation of a conditioning protocol based on mechanical stimuli and EMG biofeedback. However, in contrast to literature, preliminary results show that subjects were unable to successfully obtain task-dependent modulation of their soleus short-latency stretch reflex magnitude

A Haptic Surface Robot Interface for Large-Format Touchscreen Displays

This thesis presents the design for a novel haptic interface for large-format touchscreens. Techniques such as electrovibration, ultrasonic vibration, and external braked devices have been developed by other researchers to deliver haptic feedback to touchscreen users. However, these methods do not address the need for spatial constraints that only restrict user motion in the direction of the constraint. This technology gap contributes to the lack of haptic technology available for touchscreen-based upper-limb rehabilitation, despite the prevalent use of haptics in other forms of robotic rehabilitation. The goal of this thesis is to display kinesthetic haptic constraints to the touchscreen user in the form of boundaries and paths, which assist or challenge the user in interacting with the touchscreen. The presented prototype accomplishes this by steering a single wheel in contact with the display while remaining driven by the user. It employs a novel embedded force sensor, which it uses to measure the interaction force between the user and the touchscreen. The haptic response of the device is controlled using this force data to characterize user intent. The prototype can operate in a simulated free mode as well as simulate rigid and compliant obstacles and path constraints. A data architecture has been created to allow the prototype to be used as a peripheral add-on device which reacts to haptic environments created and modified on the touchscreen. The long-term goal of this work is to create a haptic system that enables a touchscreen-based rehabilitation platform for people with upper limb impairments

A Simple Position Sensing Device for Upper Limb Rehabilitation

Stroke is a leading cause of disability which can affect shoulder and elbow movements which are necessary for reaching activities in numerous daily routines. Rehabilitation under the supervision of physiotherapists in healthcare settings is to encourage the recovery process. Unfortunately, these sessions are often labor-intensive and limited intervention time between physiotherapist and the stroke patient due to staff constraints. Dedicated robotic devices have been developed to overcome this issues. However, the high cost of these robots is a major concern as it limits their cost-benefit profiles, thus impeding large scale implementation. This paper presents a simple and portable unactuated interactive rehabilitation device for upper limb rehabilitation purposes. This device has been developed by using a conventional mouse integrated with three interactive training modules, namely the Triangle, Square, and Circle modules intended for training shoulder and elbow movements. Results from five healthy subjects showed the more deviation from the path will be happened when the subject move their hand to the other side of their dominant hand. Besides, the shape of the module that includes combination of X and Y axis direction is more difficult compare to either X or Y axis

Bioinspired robotic rehabilitation tool for lower limb motor learning after stroke

Mención Internacional en el título de doctorEsta tesis doctoral presenta, tras repasar la marcha humana, las principales patologíıas y condiciones que la afectan, y los distintos enfoques de rehabilitación con la correspondiente implicación neurofisiológica, el camino de investigación que desemboca en la herramienta robótica de rehabilitación y las terapias que se han desarrollado en el marco de los proyectos europeos BioMot: Smart Wearable Robots with Bioinspired Sensory-Motor Skills y HANK: European advanced exoskeleton for rehabilitation of Acquired Brain Damage (ABD) and/or spinal cord injury’s patients, y probado bajo el paraguas del proyecto europeo ASTONISH: Advancing Smart Optical Imaging and Sensing for Health y el proyecto nacional ASSOCIATE: A comprehensive and wearable robotics based approach to the rehabilitation and assistance to people with stroke and spinal cord injury.This doctoral thesis presents, after reviewing human gait, the main pathologies and conditions that affect it, and the different rehabilitation approaches with the corresponding neurophysiological implications, the research journey that leads to the development of the rehabilitation robotic tool, and the therapies that have been designed, within the framework of the European projects BioMot: Smart Wearable Robots with Bioinspired Sensory-Motor Skills and HANK: European advanced exoskeleton for rehabilitation of Acquired Brain Damage (ABD) and/or spinal cord injury’s patients and tested under the umbrella of the European project ASTONISH: Advancing Smart Optical Imaging and Sensing for Health and the national project ASSOCIATE: A comprehensive and wearable robotics based approach to the rehabilitation and assistance to people with stroke and spinal cord injury.This work has been carried out at the Neural Rehabilitation Group (NRG), Cajal Institute, Spanish National Research Council (CSIC). The research presented in this thesis has been funded by the Commission of the European Union under the BioMot project - Smart Wearable Robots with Bioinspired Sensory-Motor Skills (Grant Agreement number IFP7-ICT - 611695); under HANK Project - European advanced exoskeleton for rehabilitation of Acquired Brain Damage (ABD) and/or spinal cord injury’s patients (Grant Agreements number H2020-EU.2. - PRIORITY ’Industrial leadership’ and H2020-EU.3. - PRIORITY ’Societal challenges’ - 699796); also under the ASTONISH Project - Advancing Smart Optical Imaging and Sensing for Health (Grant Agreement number H2020-EU.2.1.1.7. - ECSEL - 692470); with financial support of Spanish Ministry of Economy and Competitiveness (MINECO) under the ASSOCIATE project - A comprehensive and wearable robotics based approach to the rehabilitation and assistance to people with stroke and spinal cord injury (Grant Agreement number 799158449-58449-45-514); and with grant RYC-2014-16613, also by Spanish Ministry of Economy and Competitiveness.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Fernando Javier Brunetti Fernández.- Secretario: Dorin Sabin Copaci.- Vocal: Antonio Olivier

Adaptive Robot Mediated Upper Limb Training Using Electromyogram Based Muscle Fatigue Indicators

© 2020 Thacham Poyil et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Studies on improving the adaptability of upper limb rehabilitation training do not often consider the implications of muscle fatigue sufficiently. In this study, electromyogram features were used as fatigue indicators in a context of human-robot interaction, and were utilised for auto-adaptation of the task difficulty, which resulted in a prolonged training interaction.The electromyogram data was collected from three gross-muscles of the upper limb in 30 healthy participants.The experiment followed a protocol for increasing the muscle strength by progressive strength training, that was an implementation of a known method in sports science for muscle training, in a new domain of robotic adaptation in muscle training.The study also compared how the change in task difficulty levels was perceived by the participants, when the robot adjusted the difficulty, when the difficulty was manually adjusted, and also when there was no difficulty adjustment at all.Three experimental conditions were chosen, one benefiting from robotic adaptation (Intervention group) and the other two presenting control groups 1 and 2.The results indicated that the participants could perform a prolonged progressive strength training exercise with more repetitions with the help of a fatigue-based robotic adaptation, compared to the training interactions, which were based on manual/no adaptation.This study showed that using fatigue indicators, it is possible to alter the level of challenge, and thus, increase the interaction time.The results of the study are expected to be extended to stroke patients in the future by utilizing the potential for adapting the training difficulty according to the patient's muscular state, and also to have large number repetitions in a robot-assisted training environment.Peer reviewe

Usability of Upper Limb Electromyogram Features as Muscle Fatigue Indicators for Better Adaptation of Human-Robot Interactions

Human-robot interaction (HRI) is the process of humans and robots working together to accomplish a goal with the objective of making the interaction beneficial to humans. Closed loop control and adaptability to individuals are some of the important acceptance criteria for human-robot interaction systems. While designing an HRI interaction scheme, it is important to understand the users of the system and evaluate the capabilities of humans and robots. An acceptable HRI solution is expected to be adaptable by detecting and responding to the changes in the environment and its users. Hence, an adaptive robotic interaction will require a better sensing of the human performance parameters. Human performance is influenced by the state of muscular and mental fatigue during active interactions. Researchers in the field of human-robot interaction have been trying to improve the adaptability of the environment according to the physical state of the human participants. Existing human-robot interactions and robot assisted trainings are designed without sufficiently considering the implications of fatigue to the users. Given this, identifying if better outcome can be achieved during a robot-assisted training by adapting to individual muscular status, i.e. with respect to fatigue, is a novel area of research. This has potential applications in scenarios such as rehabilitation robotics. Since robots have the potential to deliver a large number of repetitions, they can be used for training stroke patients to improve their muscular disabilities through repetitive training exercises. The objective of this research is to explore a solution for a longer and less fatiguing robot-assisted interaction, which can adapt based on the muscular state of participants using fatigue indicators derived from electromyogram (EMG) measurements. In the initial part of this research, fatigue indicators from upper limb muscles of healthy participants were identified by analysing the electromyogram signals from the muscles as well as the kinematic data collected by the robot. The tasks were defined to have point-to-point upper limb movements, which involved dynamic muscle contractions, while interacting with the HapticMaster robot. The study revealed quantitatively, which muscles were involved in the exercise and which muscles were more fatigued. The results also indicated the potential of EMG and kinematic parameters to be used as fatigue indicators. A correlation analysis between EMG features and kinematic parameters revealed that the correlation coefficient was impacted by muscle fatigue. As an extension of this study, the EMG collected at the beginning of the task was also used to predict the type of point-to-point movements using a supervised machine learning algorithm based on Support Vector Machines. The results showed that the movement intention could be detected with a reasonably good accuracy within the initial milliseconds of the task. The final part of the research implemented a fatigue-adaptive algorithm based on the identified EMG features. An experiment was conducted with thirty healthy participants to test the effectiveness of this adaptive algorithm. The participants interacted with the HapticMaster robot following a progressive muscle strength training protocol similar to a standard sports science protocol for muscle strengthening. The robotic assistance was altered according to the muscular state of participants, and, thus, offering varying difficulty levels based on the states of fatigue or relaxation, while performing the tasks. The results showed that the fatigue-based robotic adaptation has resulted in a prolonged training interaction, that involved many repetitions of the task. This study showed that using fatigue indicators, it is possible to alter the level of challenge, and thus, increase the interaction time. In summary, the research undertaken during this PhD has successfully enhanced the adaptability of human-robot interaction. Apart from its potential use for muscle strength training in healthy individuals, the work presented in this thesis is applicable in a wide-range of humanmachine interaction research such as rehabilitation robotics. This has a potential application in robot-assisted upper limb rehabilitation training of stroke patients