Physiological Responses During Hybrid BNCI Control of an Upper-Limb Exoskeleton

Abstract

When combined with assistive robotic devices, such as wearable robotics, brain/neural-computer interfaces (BNCI) have the potential to restore the capabilities of handicapped people to carry out activities of daily living. To improve applicability of such systems, workload and stress should be reduced to a minimal level. Here, we investigated the user’s physiological reactions during the exhaustive use of the interfaces of a hybrid control interface. Eleven BNCI-naive healthy volunteers participated in the experiments. All participants sat in a comfortable chair in front of a desk and wore a whole-arm exoskeleton as well as wearable devices for monitoring physiological, electroencephalographic (EEG) and electrooculographic (EoG) signals. The experimental protocol consisted of three phases: (i) Set-up, calibration and BNCI training; (ii) Familiarization phase; and (iii) Experimental phase during which each subject had to perform EEG and EoG tasks. After completing each task, the NASA-TLX questionnaire and self-assessment manikin (SAM) were completed by the user. We found significant differences (p-value < 0.05) in heart rate variability (HRV) and skin conductance level (SCL) between participants during the use of the two different biosignal modalities (EEG, EoG) of the BNCI. This indicates that EEG control is associated with a higher level of stress (associated with a decrease in HRV) and mental work load (associated with a higher level of SCL) when compared to EoG control. In addition, HRV and SCL modulations correlated with the subject’s workload perception and emotional responses assessed through NASA-TLX questionnaires and SAM.

A Modular Mobile Robotic Platform to Assist People with Different Degrees of Disability

“…In addition, new biosignals and processing techniques can be integrated. The performance, signal processing, and adaptation of the different physiological signals of the system have been tested in several studies [11][12][13][14][15].…”

“…A GSR sensor, developed by Shimmer, measures the skin conductivity between two reusable electrodes mounted to two fingers of one hand. These data are used, together with the EKG and the respiratory rate, to estimate the affective state of the user [12]. GSR is a common measure in psychophysiological paradigms and therefore often used in affective state detection.…”

“…An upper limb exoskeleton was designed with five active degrees of freedom corresponding to the following arm movements: shoulder abduction/adduction, shoulder flexion/extension, shoulder internal/external rotation, elbow flexion/extension, and wrist pronation/supination [11,12,21,22]. This device allows the user's right arm to be moved to reach objects, thus facilitating the performance of ADLs (Figure 1).…”

“…The integration of environmental data acquired by 3D sensors and user intentions has been evaluated in several studies [11][12][13][14][15]. The AIDE system also incorporates an activity recognition algorithm to improve the performance of the control interfaces.…”

Assistance Robotics and Biosensors 2019

“…In [6], the authors propose a method to detect the appearance of sudden obstacles from electroencephalographic (EEG) signals. This method could be applied in the supervision of gait rehabilitation using exoskeletons.…”