Influence of Muscle Fatigue on Electromyogram-Kinematic Correlation During Robot-Assisted Upper Limb Training

© The Author(s) 2020. Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us. sagepub.com/en-us/nam/open-access-at-sage).Introduction: Studies on adaptive robot-assisted upper limb training interactions do not often consider the implications of muscle fatigue sufficiently. Methods: In order to explore this, we initially assessed muscle fatigue in 10 healthy subjects using electromyogram features (average power and median power frequency) during an assist-as-needed interaction with HapticMASTER robot. Spearman’s correlation study was conducted between EMG average power and kinematic force components. Since the robotic assistance resulted in a variable fatigue profile across participants, a completely tiring experiment, without a robot in the loop, was also designed to confirm the results. Results: A significant increase in average power and a decrease in median frequency were observed in the most active muscles. Average power in the frequency band of 0.8-2.5Hz and median frequency in the band of 20-450Hz are potential fatigue indicators. Also, comparing the correlation coefficients across trials indicated that correlation was reduced as the muscles were fatigued. Conclusions: Robotic assistance based on user’s performance has resulted in lesser muscle fatigue, which caused an increase in the EMG-force correlation. We now intend to utilize the electromyogram and kinematic features for the auto-adaptation of therapeutic human-robot interactions.Peer reviewedFinal Published versio

Application of support vector machines to detect hand and wrist gestures using a myoelectric armband

Farshid Amirabdollahian, Michael Walters, ‘Application of support vector machines to detect hand and wrist gestures using a myoelectric armband’, paper presented at the International conference on rehabilitation robotics: ICORR2017, London, UK, 17-21 July, 2017.The propose of this study was to assess the feasibility of using support vector machines in analysing myoelectric signals acquired using an off the shelf device, the Myo armband from Thalmic Lab. Background: With the technological advances in sensing human motion, and its potential to drive and control mechanical interfaces remotely or to be used as input interfaces, a multitude of input mechanisms are used to link actions between the human and the robot. In this study we explored the feasibility of using human arm’s myoelectric signals with the aim of identifying a number of gestures automatically. Material and methods: Participants (n = 26) took part in a study with the aim to assess the gesture detection accuracy using myoelectric signals. The Myo armband was used worn on the forearm. The session was divided into three phases, familiarisation: where participants learned how to use the armband, training: when participants reproduced a number of random gestures presented on screen to train our machine learning algorithm; and recognition: when gestures presented on screen were reproduced by participants, and simultaneously recognised using the machine learning routines. Support vector machines were used to train a model using participant training values, and to recognise gestures produced by the same participants. Different Kernel functions and electrode combinations were studied. Also we contrasted different lengths of training values versus different lengths for the recognition samples. Results: One participant did not complete the study due to technical errors during the session. The remaining (n = 25) participants completed the study allowing to calculate individual accuracy for grasp detection. The overall accuracy was 94.9% with data from 8 electrodes , and 72% where only four of the electrodes were used. The linear kernel outperformed the polynomial, and radial basis function. Exploring the number of training samples versus the achieved recognition accuracy, results identified acceptable accuracies (> 90%) for training around 3.5s, and recognising grasp episodes of around 0.2s long. The best recognised grasp was the hand closed (97.6%), followed by cylindrical grasp (96.8%), the lateral grasp (94%) and tripod (92%). Discussions: The recognition accuracy for the grasp performed is similar to our earlier work where a mechatronic device was used to perform, record and recognise these grasps. This is an interesting observation, as our previous effort in aligning the kinematic and biological signals had not found statistically significant links between the two. However, when the outcome of both is used as a label for identification, in this case gesture, it appears that machine learning is able to identify both kinematic and electrophysiological events with similar accuracy. Future work: The current study considers use of support vector machines for identifying human grasps based on myoelectric signals acquired from an off the shelf device. Due to the length of sessions in the experiment, we were only able to gather 5 seconds of training data and at a 50Hz sampling frequency. This provided us with limited amount of training data so we were not able to test shorter training times (< 2.5s). The device is capable of faster sampling, up to 200Hz and our future studies will benefit from this sampling rate and longer training sessions to explore if we can identify gestures using smaller amount of training data. These results allows us to progress to the next stage of work where the Myo armband is used in the context of robot-mediated stroke rehabilitation.Peer reviewedFinal Accepted Versio

Analysis of the results from use of haptic peg-in-hole task for assessment in neurorehabilitation

Original article can be found at : http://iospress.metapress.com/ Copyright IOS PressHaptic and robotic technologies have the potential to provide assessment during interaction with humans. This manuscript presents our earlier research during the I-Match project where a haptic peg-in-hole test was used in order to compare between healthy volunteers' performance and those with neurological impairment. Subjects all performed a series of haptic virtual peg-in-hole tasks with varying degrees of difficulty determined by the hole diameter. Haptic instrument, Phantom Desktop 1.5, allowed for recording of biomechanical data which is used to present some variant features between the two subject groups. This paper analyses the placement time, maximum peg transfer velocity, collision forces recorded during peg placement and also insertion accuracy. The first three parameters showed statistically significant differences between the two groups while the last, insertion accuracy, showed insignificant differences (p = 0.152). This is thought to be due to the large clearance value between the smallest hole diameter and the peg. To identify differences between the haptic peg-in-hole and the established NHPT, we are currently in process of conducting a further experiment with a haptic replica of the NHPT test, in order to investigate effects resulting from addition of haptic force feedback compared to the original NHPT test, as well as allowing to explore influences caused by the 1 mm clearance value as originally proposed by Wade.Furthermore, in order to investigate if this method can identify differences between subjects with different neurological conditions, a larger group of subjects with neurological conditions such as stroke, multiple sclerosis, and traumatic brain injury is required to explore potency of this approach for identifying differences between these different conditions.Peer reviewe

Towards Safe and Trustworthy Social Robots : Ethical Challenges and Practical Issues

Maha Salem, Gabriella Lakatos, Farshid Amirabdollahian, K. Dautenhahn, ‘Towards Safe and Trustworthy Social Robots: Ethical Challenges and Practical Issues’, paper presented at the 7th International Conference on Social Robotics, Paris, France, 26-30 October, 2015.As robots are increasingly developed to assist humans so- cially with everyday tasks in home and healthcare settings, questions regarding the robot's safety and trustworthiness need to be addressed. The present work investigates the practical and ethical challenges in de- signing and evaluating social robots that aim to be perceived as safe and can win their human users' trust. With particular focus on collaborative scenarios in which humans are required to accept information provided by the robot and follow its suggestions, trust plays a crucial role and is strongly linked to persuasiveness. Accordingly, human-robot trust can directly aect people's willingness to cooperate with the robot, while under- or overreliance may have severe or even dangerous consequences. Problematically, investigating trust and human perceptions of safety in HRI experiments proves challenging in light of numerous ethical con- cerns and risks, which this paper aims to highlight and discuss based on experiences from HRI practice.Peer reviewe

Would You Trust a (Faulty) Robot? : Effects of Error, Task Type and Personality on Human-Robot Cooperation and Trust

How do mistakes made by a robot affect its trustworthiness and acceptance in human-robot collaboration? We investigate how the perception of erroneous robot behavior may influence human interaction choices and the willingness to cooperate with the robot by following a number of its unusual requests. For this purpose, we conducted an experiment in which participants interacted with a home companion robot in one of two experimental conditions: (1) the correct mode or (2) the faulty mode. Our findings reveal that, while significantly affecting subjective perceptions of the robot and assessments of its reliability and trustworthiness, the robot's performance does not seem to substantially influence participants' decisions to (not) comply with its requests. However, our results further suggest that the nature of the task requested by the robot, e.g. whether its effects are revocable as opposed to irrevocable, has a signicant im- pact on participants' willingness to follow its instructions

Application of support vector machines in detecting hand grasp gestures using a commercially off the shelf wireless myoelectric armband

©2017 IEEE.The propose of this study was to assess the feasibility of using support vector machines in analysing myoelectric signals acquired using an off the shelf device, the Myo armband from Thalmic Lab, when performing hand grasp gestures. Participants (n = 26) took part in the study wearing the armband and producing a series of required gestures. Support vector machines were used to train a model using participant training values, and to classify gestures produced by the same participants. Different Kernel functions and electrode combinations were studied. Also we contrasted different lengths of training values versus different lengths for the classification samples. The overall accuracy was 94.9% with data from 8 electrodes, and 72% where only four of the electrodes were used. The linear kernel outperformed the polynomial, and radial basis function. Exploring the number of training samples versus the achieved classification accuracy, results identified acceptable accuracies (> 90%) for training around 2.5s, and recognising grasp with 0.2s of acquired data. The best recognised grasp was the hand closed (97.6%), followed by cylindrical grasp (96.8%), the lateral grasp (93.2%) and tripod (92%). These results allows us to progress to the next stage of work where the Myo armband is used in the context of robot-mediated stroke rehabilitation and also involves more dynamic interactions as well as gross upper arm movements.Final Published versio

Humans' Perception of a Robot Moving Using a Slow in and Slow Out Velocity Profile

© 2019 IEEE - All rights reservedHumans need to understand and trust the robots they are working with. We hypothesize that how a robot moves can impact people’s perception and their trust. We present a methodology for a study to explore people’s perception of a robot using the animation principle of slow in, slow out—to change the robot’s velocity profile versus a robot moving using a linear velocity profile. Study participants will interact with the robot within a home context to complete a task while the robot moves around the house. The participants’ perceptions of the robot will be recorded using the Godspeed Questionnaire. A pilot study shows that it is possible to notice the difference between the linear and the slow in, slow out velocity profiles, so the full experiment planned with participants will allow us to compare their perceptions based on the two observable behaviors.Final Accepted Versio

EEG Spectral Feature Modulations Associated with Fatigue in Robot-Mediated Upper Limb Gross and Fine Motor Interactions

© 2022 Dissanayake, Steuber and Amirabdollahian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). https://creativecommons.org/licenses/by/4.0/This paper investigates the EEG spectral feature modulations associated with fatigue induced by robot-mediated upper limb gross and fine motor interactions. Twenty healthy participants were randomly assigned to perform a gross motor interaction with Haptic MASTER or a fine motor interaction with SCRIPT passive orthosis for 20 minutes or until volitional fatigue. Relative and ratio band power measures were estimated from the EEG data recorded before and after the robot-mediated interactions. Paired samples t-tests found a significant increase in the relative alpha band power and a significant decrease in the relative delta band power due to the fatigue induced by the robot-mediated gross and fine motor interactions. The gross motor task also significantly increased the (θ + α)/β and α/β ratio band power measures, whereas the fine motor task increased the relative theta band power. Furthermore, the robot-mediated gross movements mostly changed the EEG activity around the central and parietal brain regions, whereas the fine movements mostly changed the EEG activity around the frontopolar and central brain regions. The subjective ratings suggest that the gross motor task may have induced physical fatigue, whereas the fine motor task may have induced mental fatigue. Therefore, findings affirm that changes to localised brain activity patterns indicate fatigue developed from the robot-mediated interactions. It can also be concluded that the regional differences in the prominent EEG spectral features are most likely due to the differences in the nature of the task (fine/gross motor and distal/proximal upper limb) that may have differently altered an individual’s physical and mental fatigue level. The findings could potentially be used in future to detect fatigue during robot-mediated post-stroke therapies.Peer reviewedFinal Published versio

Script: Usability of Hand & Wrist Tele-Rehabilitation for Stroke Patients Involving Personal Tele-Robotics

We present the overall user experience designed for supporting rehabilitation of stroke patients at home. Patients use a robotic hand (orthosis) to control therapeutic games and a touch screen for the UI. They are supervised remotely by a therapist who uses a similar interface from their desk. The system includes therapeutic games and user interfaces (UIs) for both patients and therapists. The concept and design of these UIs were implemented during the first year of the SCRIPT projectPeer reviewe

Analysis of EEG Microstates During Execution of a Nine Hole Peg Test

© 2023, IARIA.Abstract—EEG microstates are brief periods of time during which the brain’s electrical activity remains stable. The analysis of EEG microstates can help to identify the background neuronal activity at the millisecond level. The main objective of this study is to observe changes in brain microstates by varying demand during different experiment phases, involving a fatiguing exercise. The hypothesis explored in this paper is that resting state and fine motor states involve different neural assemblies and that physical fatigue induced using a wrist dumbbell flexion/extension exercise impacts these microstates. An experiment is conducted with 5 healthy participants, exploring this. Three distinct microstates are observed during the resting state and a separate set of 3 states are observed during the Nine Hole Peg Test. Changes are assessed by utilising microstate parameters such as occurrence, coverage, duration, and global explained variance. It is found that the coverage of microstate C for resting states decreases for all the participants after the dumbbell exercise. During the fine-motor task, the coverage of microstate MS3 decreases for all participants except one. These results support the involvement of different neural assemblies, but also highlight the potential that physical fatigue can be observed and identified by assessing changes in microstate features, in this case, a parameter such as coverage.Peer reviewe