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URL: https://dx.doi.org/DOI:10.17504/protocols.io.5hrg356

Authors: Azeemsha Thacham Poyil, Farshid Amirabdollahian

Summary: This is a protocol for an progressive muscle strength training in a new domain of adaptive robot-assisted training interactions. The protocol was defined based on sports science literature, where the participants would be performing maximum voluntary contraction (MVC) trials at first, then taking a recovery break, then starting with a low-intensity task, and then gradually increasing the difficulty level. The protocol included a preparation stage, followed by initial measurements, a familiarisation session, and finally a performance session. Thirty (17 males, 13 females) healthy participants of at least 18 years old with no history of injury to the upper limb and back were involved in this experiment. Participants were students or staff members of University of Hertfordshire or other volunteers from outside the university. The total duration of the experiment, including the set-up time for each participant, was normally 40-55 minutes. A questionnaire was given as part of the experiment. The participants were asked to fill in a part of the questionnaire at the beginning of the experiment. They were also requested to update the fatigue state in the questionnaire after finishing the experiment.The electromyogram measurements were taken using an EMG acquisition device (g.USBamp amplifier) from g.tec medical engineering GmbH. The data acquisition parameters (sampling rate, channel selection and so on) of the device were configured using Simulink 2017b.Three EMG electrode channels were configured in bipolar mode with a sampling frequency of 1200Hz. Electromyogram (EMG) signals were collected from 3 upper limb muscles (Biceps Brachii, Anterior Deltoid, and Middle Deltoid) of the participants during a robotic interaction. EMG electrodes for Biceps Brachii muscles were connected to Ports 11-12 of g.USBamp amplifier, Frontal Deltoid muscles to Ports 13-14 and Middle Deltoid muscles to Ports 15-16. The experiment set-up included the HapticMaster robotic interface configured for a rowing task. The tasks involved upper limb exercises, which simulated rowing, using a robot arm (HapticMaster robot) as directed by visual instructions on screen and audio cues. In order to support an aesthetically pleasing interactive task for the participants, an animated rowing environment embedded with audio cues and haptic sensation of underwater viscosity were created using the HapticMaster robot. The background on a wide-screen 43 inch LCD monitor would display the front-end of a rowing boat with flowing water, which would potentially motivate the participants for an active involvement in the task. A suitable audio for water flow was played in the background. The HapticMaster robot was programmed to deliver different viscosities under water and above water while rowing. The starting time, the break period and the stopping time of the experiment were guided by audio cues. The task involved moving a robotic end-effector, while an animated boat rowing environment running in front of the participant on an LCD monitor. Kinematic measurements by the robot were logged into separate csv files for each participant.There were be 3 groups of participants in this study. Control 1 Group (Group A) participants did not receive any adaptation from the robot during the interaction and were given break periods at regular intervals. Intervention Group (Group B) interacted with the adaptive robotic environment, which was designed to adjust the difficulty level of the training exercise based on EMG based fatigue indicators. Control 2 Group (Group C) participants had a similar environment as Group B, but the environment only adapted based on the subject-reported fatigue. The participants were asked to continue the exercise until they felt very tired or until they reported fatigue 3 times or until the maximum feasible robotic resistance was reached. Participants were allowed to stop the session in cases discomfort.

Associated Publications: Poyil AT, Steuber V, Amirabdollahian F (2020) Adaptive robot mediated upper limb training using electromyogram-based muscle fatigue indicators. PLoS ONE 15(5): e0233545. doi: 10.1371/journal.pone.0233545

Affiliations: University of Hertfordshire, University of Hertfordshire

External URL: https://doi.org/10.1371/journal.pone.0233545

Version: 1

Publication Date: 2019

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