Measuring mental workload in assistive wearable devices: a review

User-Centered Evaluation of the Wearable Walker Lower Limb Exoskeleton; Preliminary Assessment Based on the Experience Protocol

Using lower limb exoskeletons provides potential advantages in terms of productivity and safety associated with reduced stress. However, complex issues in human-robot interactions are still open, such as the physiological effects of exoskeletons and the impact on the user's subjective experience. In this work, an innovative exoskeleton, the Wearable Walker, is assessed using the EXPERIENCE benchmarking protocol from the EUROBENCH project. The Wearable Walker is a lower-limb exoskeleton that enhances human abilities, such as carrying loads. The device uses a unique control approach called Blend Control that provides smooth assistance torques. It operates two models simultaneously, one in the case in which the left foot is grounded and another for the grounded right foot. These models generate assistive torques combined to provide continuous and smooth overall assistance, preventing any abrupt changes in torque due to model switching. The EXPERIENCE protocol consists of walking on flat ground while gathering physiological signals, such as heart rate, its variability, respiration rate, and galvanic skin response, and completing a questionnaire. The test was performed with five healthy subjects. The scope of the present study is twofold: to evaluate the specific exoskeleton and its current control system to gain insight into possible improvements and to present a case study for a formal and replicable benchmarking of wearable robots.

Eye-Tracking in Physical Human-Robot Interaction: Mental Workload and Performance Prediction

BACKGROUND

In Physical Human-Robot Interaction (pHRI), the need to learn the robot's motor-control dynamics is associated with increased cognitive load. Eye-tracking metrics can help understand the dynamics of fluctuating mental workload over the course of learning.

OBJECTIVE

The aim of this study was to test eye-tracking measures' sensitivity and reliability to variations in task difficulty, as well as their performance-prediction capability, in physical human-robot collaboration tasks involving an industrial robot for object comanipulation.

METHODS

Participants (9M, 9F) learned to coperform a virtual pick-and-place task with a bimanual robot over multiple trials. Joint stiffness of the robot was manipulated to increase motor-coordination demands. The psychometric properties of eye-tracking measures and their ability to predict performance was investigated.

RESULTS

Stationary Gaze Entropy and pupil diameter were the most reliable and sensitive measures of workload associated with changes in task difficulty and learning. Increased task difficulty was more likely to result in a robot-monitoring strategy. Eye-tracking measures were able to predict the occurrence of success or failure in each trial with 70% sensitivity and 71% accuracy.

CONCLUSION

The sensitivity and reliability of eye-tracking measures was acceptable, although values were lower than those observed in cognitive domains. Measures of gaze behaviors indicative of visual monitoring strategies were most sensitive to task difficulty manipulations, and should be explored further for the pHRI domain where motor-control and internal-model formation will likely be strong contributors to workload.

APPLICATION

Future collaborative robots can adapt to human cognitive state and skill-level measured using eye-tracking measures of workload and visual attention.

Classification of mental workload using brain connectivity and machine learning on electroencephalogram data

Mental workload refers to the cognitive effort required to perform tasks, and it is an important factor in various fields, including system design, clinical medicine, and industrial applications. In this paper, we propose innovative methods to assess mental workload from EEG data that use effective brain connectivity for the purpose of extracting features, a hierarchical feature selection algorithm to select the most significant features, and finally machine learning models. We have used the Simultaneous Task EEG Workload (STEW) dataset, an open-access collection of raw EEG data from 48 subjects. We extracted brain-effective connectivities by the direct directed transfer function and then selected the top 30 connectivities for each standard frequency band. Then we applied three feature selection algorithms (forward feature selection, Relief-F, and minimum-redundancy-maximum-relevance) on the top 150 features from all frequencies. Finally, we applied sevenfold cross-validation on four machine learning models (support vector machine (SVM), linear discriminant analysis, random forest, and decision tree). The results revealed that SVM as the machine learning model and forward feature selection as the feature selection method work better than others and could classify the mental workload levels with accuracy equal to 89.53% (± 1.36).

Mental workload assessment by monitoring brain, heart, and eye with six biomedical modalities during six cognitive tasks

Introduction

The efficiency and safety of complex high precision human-machine systems such as in aerospace and robotic surgery are closely related to the cognitive readiness, ability to manage workload, and situational awareness of their operators. Accurate assessment of mental workload could help in preventing operator error and allow for pertinent intervention by predicting performance declines that can arise from either work overload or under stimulation. Neuroergonomic approaches based on measures of human body and brain activity collectively can provide sensitive and reliable assessment of human mental workload in complex training and work environments.

Methods

In this study, we developed a new six-cognitive-domain task protocol, coupling it with six biomedical monitoring modalities to concurrently capture performance and cognitive workload correlates across a longitudinal multi-day investigation. Utilizing two distinct modalities for each aspect of cardiac activity (ECG and PPG), ocular activity (EOG and eye-tracking), and brain activity (EEG and fNIRS), 23 participants engaged in four sessions over 4 weeks, performing tasks associated with working memory, vigilance, risk assessment, shifting attention, situation awareness, and inhibitory control.

Results

The results revealed varying levels of sensitivity to workload within each modality. While certain measures exhibited consistency across tasks, neuroimaging modalities, in particular, unveiled meaningful differences between task conditions and cognitive domains.

Discussion

This is the first comprehensive comparison of these six brain-body measures across multiple days and cognitive domains. The findings underscore the potential of wearable brain and body sensing methods for evaluating mental workload. Such comprehensive neuroergonomic assessment can inform development of next generation neuroadaptive interfaces and training approaches for more efficient human-machine interaction and operator skill acquisition.

The levels and related factors of mental workload among nurses: A systematic review and meta-analysis

AIM

The aim was to determine the overall levels and related factors of mental workload assessed using the NASA-TLX tool among nurses.

BACKGROUND

Mental workload is a key element that affects nursing performance. However, there exists no review regarding mental workload assessed using the NASA-TLX tool, focusing on nurses.

DESIGN

A systematic review and meta-analysis.

DATA SOURCES

PubMed, MEDLINE, Web of Science, EMBASE, PsycINFO, Scopus, CINAHL, CNKI, CBM, Weipu and WanFang databases were searched from 1 January 1998 to 30 February 2022.

REVIEW METHODS

Following the PRISMA statement recommendations, review methods resulted in 31 quantitative studies retained for inclusion which were evaluated with the evaluation criteria for observational studies as recommended by the Agency for Healthcare Research and Quality. The data were pooled and a random-effects meta-analysis conducted.

RESULTS

Findings showed the pooled mental workload score was 65.24, and the pooled prevalence of high mental workload was 54%. Subgroup analysis indicated nurses in developing countries and emergency departments experienced higher mental workloads, and the mental workloads of front-line nurses increased significantly during the COVID-19 pandemic.

CONCLUSION

These findings highlight that nurses experience high mental workloads as assessed using the NASA-TLX tool and there is an urgent need to explore interventions to decrease their mental workloads.

Dry EEG measurement of P3 to evaluate cognitive load during sitting, standing, and walking

Combining brain imaging with dual-task paradigms provides a quantitative, direct metric of cognitive load that is agnostic to the motor task. This work aimed to quantitatively assess cognitive load during activities of daily living-sitting, standing, and walking-using a commercial dry encephalography headset. We recorded participants' brain activity while engaging in a stimulus paradigm that elicited event-related potentials. The stimulus paradigm consisted of an auditory oddball task in which participants had to report the number of oddball tones that were heard during each motor task. We extracted the P3 event-related potential, which is inversely proportional to cognitive load, from EEG signals in each condition. Our main findings showed that P3 was significantly lower during walking compared to sitting (p = .039), suggesting that cognitive load was higher during walking compared to the other activities. There were no significant differences in P3 between sitting and standing. Head motion did not have a significant impact on the measurement of cognitive load. This work validates the use of a commercial dry-EEG headset for measuring cognitive load across different motor tasks. The ability to accurately measure cognitive load in dynamic activities opens new avenues for exploring cognitive-motor interactions in individuals with and without motor impairments. This work highlights the potential of dry EEG for measuring cognitive load in naturalistic settings.

A scoping review of eye tracking metrics used to assess visuomotor behaviours of upper limb prosthesis users

Advanced upper limb prostheses aim to restore coordinated hand and arm function. However, this objective can be difficult to quantify as coordinated movements require an intact visuomotor system. Eye tracking has recently been applied to study the visuomotor behaviours of upper limb prosthesis users by enabling the calculation of eye movement metrics. This scoping review aims to characterize the visuomotor behaviours of upper limb prosthesis users as described by eye tracking metrics, to summarize the eye tracking metrics used to describe prosthetic behaviour, and to identify gaps in the literature and potential areas for future research. A review of the literature was performed to identify articles that reported eye tracking metrics to evaluate the visual behaviours of individuals using an upper limb prosthesis. Data on the level of amputation, type of prosthetic device, type of eye tracker, primary eye metrics, secondary outcome metrics, experimental task, aims, and key findings were extracted. Seventeen studies were included in this scoping review. A consistently reported finding is that prosthesis users have a characteristic visuomotor behaviour that differs from that of individuals with intact arm function. Visual attention has been reported to be directed more towards the hand and less towards the target during object manipulation tasks. A gaze switching strategy and delay to disengage gaze from the current target has also been reported. Differences in the type of prosthetic device and experimental task have revealed some distinct gaze behaviours. Control factors have been shown to be related to gaze behaviour, while sensory feedback and training interventions have been demonstrated to reduce the visual attention associated with prosthesis use. Eye tracking metrics have also been used to assess the cognitive load and sense of agency of prosthesis users. Overall, there is evidence that eye tracking is an effective tool to quantitatively assess the visuomotor behaviour of prosthesis users and the recorded eye metrics are sensitive to change in response to various factors. Additional studies are needed to validate the eye metrics used to assess cognitive load and sense of agency in upper limb prosthesis users.

Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines

BACKGROUND AND OBJECTIVE

Mental workload (MWL) is a relevant construct involved in all cognitively demanding activities, and its assessment is an important goal in many research fields. This paper aims at evaluating the reproducibility and sensitivity of MWL assessment from EEG signals considering the effects of different electrode configurations and pre-processing pipelines (PPPs).

METHODS

Thirteen young healthy adults were enrolled and were asked to perform 45 min of Simon's task to elicit a cognitive demand. EEG data were collected using a 32-channel system with different electrode configurations (fronto-parietal; Fz and Pz; Cz) and analyzed using different PPPs, from the simplest bandpass filtering to the combination of filtering, Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). The reproducibility of MWL indexes estimation and the sensitivity of their changes were assessed using Intraclass Correlation Coefficient and statistical analysis.

RESULTS

MWL assessed with different PPPs showed reliability ranging from good to very good in most of the electrode configurations (average consistency > 0.87 and average absolute agreement > 0.92). Larger fronto-parietal electrode configurations, albeit being more affected by the choice of PPPs, provide better sensitivity in the detection of MWL changes if compared to a single-electrode configuration (18 vs. 10 statistically significant differences detected, respectively).

CONCLUSIONS

The most complex PPPs have been proven to ensure good reliability (>0.90) and sensitivity in all experimental conditions. In conclusion, we propose to use at least a two-electrode configuration (Fz and Pz) and complex PPPs including at least the ICA algorithm (even better including ASR) to mitigate artifacts and obtain reliable and sensitive MWL assessment during cognitive tasks.

A tool for measuring mental workload during prosthesis use: The Prosthesis Task Load Index (PROS-TLX)

When using a upper-limb prosthesis, mental, emotional, and physical effort is often experienced. These have been linked to high rates of device dissatisfaction and rejection. Therefore, understanding and quantifying the complex nature of workload experienced when using, or learning to use, a upper-limb prosthesis has practical and clinical importance for researchers and applied professionals. The aim of this paper was to design and validate a self-report measure of mental workload specific to prosthesis use (The Prosthesis Task Load Index; PROS-TLX) that encapsulates the array of mental, physical, and emotional demands often experienced by users of these devices. We first surveyed upper-limb prosthetic limb users who confirmed the importance of eight workload constructs taken from published literature and previous workload measures. These constructs were mental demands, physical demands, visual demands, conscious processing, frustration, situational stress, time pressure and device uncertainty. To validate the importance of these constructs during initial prosthesis learning, we then asked able-bodied participants to complete a coin-placement task using their anatomical hand and then using a myoelectric prosthesis simulator under low and high mental workload. As expected, using a prosthetic hand resulted in slower movements, more errors, and a greater tendency to visually fixate the hand (indexed using eye-tracking equipment). These changes in performance were accompanied by significant increases in PROS-TLX workload subscales. The scale was also found to have good convergent and divergent validity. Further work is required to validate whether the PROS-TLX can provide meaningful clinical insights to the workload experienced by clinical users of prosthetic devices.

Wearable electroencephalography and multi-modal mental state classification: A systematic literature review

BACKGROUND

Wearable multi-modal time-series classification applications outperform their best uni-modal counterparts and hold great promise. A modality that directly measures electrical correlates from the brain is electroencephalography. Due to varying noise sources, different key brain regions, key frequency bands, and signal characteristics like non-stationarity, techniques for data pre-processing and classification algorithms are task-dependent.

METHOD

Here, a systematic literature review on mental state classification for wearable electroencephalography is presented. Four search terms in different combinations were used for an in-title search. The search was executed on the 29th of June 2022, across Google Scholar, PubMed, IEEEXplore, and ScienceDirect. 76 most relevant publications were set into context as the current state-of-the-art in mental state time-series classification.

RESULTS

Pre-processing techniques, features, and time-series classification models were analyzed. Across publications, a window length of one second was mainly chosen for classification and spectral features were utilized the most. The achieved performance per time-series classification model is analyzed, finding linear discriminant analysis, decision trees, and k-nearest neighbors models outperform support-vector machines by a factor of up to 1.5. A historical analysis depicts future trends while under-reported aspects relevant to practical applications are discussed.

CONCLUSIONS

Five main conclusions are given, covering utilization of available area for electrode placement on the head, most often or scarcely utilized features and time-series classification model architectures, baseline reporting practices, as well as explainability and interpretability of Deep Learning. The importance of a 'test battery' assessing the influence of data pre-processing and multi-modality on time-series classification performance is emphasized.

Human Mental Workload: A Survey and a Novel Inclusive Definition

Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Computer Science. However, its role in human performance prediction is assured. This work is aimed at providing a synthesis of the current state of the art in human mental workload assessment through considerations, definitions, measurement techniques as well as applications, Findings suggest that, despite an increasing number of associated research works, a single, reliable and generally applicable framework for mental workload research does not yet appear fully established. One reason for this gap is the existence of a wide swath of operational definitions, built upon different theoretical assumptions which are rarely examined collectively. A second reason is that the three main classes of measures, which are self-report, task performance, and physiological indices, have been used in isolation or in pairs, but more rarely in conjunction all together. Multiple definitions complement each another and we propose a novel inclusive definition of mental workload to support the next generation of empirical-based research. Similarly, by comprehensively employing physiological, task-performance, and self-report measures, more robust assessments of mental workload can be achieved.

Phantom Sensations Influenced by Global and Local Modifications of the Prosthetic Socket as a Potential Solution for Natural Somatosensory Feedback During Walking: A Preliminary Study of a Single Case

Following lower limb amputation, amputees are trained to walk with a prosthesis. The loss of a lower limb deprives them of essential somatosensory information, which is one of the causes of the difficulties of walking with a prosthesis. We here explored whether a solution to this lack of somatosensory feedback could come from natural sensations of the phantom limb, present in most amputees, instead of from substitutive technologies. Indeed, it is known that phantom sensations can be modulated by (i) global mechanical characteristics of the prosthesis socket, and (ii) locally applying a stimulus on an area of the residual limb. The purpose of this pilot study was to verify the feasibility of influencing phantom sensations via such socket modifications in a participant with transfemoral amputation. Four prosthetic interface conditions were studied: a rigid and a semi-rigid socket, each one with and without a focal pressure increase on a specific area of the residual limb. The results show that phantom sensations during walking were different according to the 4 interface conditions. The participant had more vivid phantom sensations in his foot and calf of which some varied as a function of the gait phases. Preliminary gait analysis with wearable sensors shows that these modifications were accompanied by changes in some gait spatiotemporal parameters. This preliminary study of single case demonstrates that phantom sensations can be modulated by the prosthetic interface and can provide natural somatosensory information dynamically varying with gait phases. Although this needs to be confirmed for a larger population of lower limb amputees, it already encourages non-painful phantom sensations to be considered early during the rehabilitation of lower limb amputees.