Capturing mental workload through physiological sensors in human–robot collaboration: a systematic literature review

Human–robot collaboration (HRC) is increasingly prevalent across various industries, promising to boost productivity, efficiency, and safety. As robotics technology advances and takes on more complex tasks traditionally performed by humans, the nature of work and the demands on workers are evolving. This shift emphasizes the need to critically integrate human factors into these interactions, as the effectiveness and safety of these systems are highly dependent on how workers cooperate with and understand robots. A significant challenge in this domain is the lack of a consensus on the most efficient way to operationalize and assess mental workload, which is crucial for optimizing HRC. In this systematic literature review, we analyze the different psychophysiological measures that can reliably capture and differentiate varying degrees of mental workload in different HRC settings. The findings highlight the crucial need for standardized methodologies in workload assessment to enhance HRC models. Ultimately, this work aims to guide both theorists and practitioners in creating more sophisticated, safe, and efficient HRC frameworks by providing a comprehensive overview of the existing literature and pointing out areas for further study.</p

WO₃ Nanoparticle-Based Conformable pH Sensor

pH is a vital physiological parameter that can be used for disease diagnosis and treatment as well as in monitoring other biological processes. Metal/metal oxide based pH sensors have several advantages regarding their reliability, miniaturization, and cost-effectiveness, which are critical characteristics for in vivo applications. In this work, WO₃ nanoparticles were electrodeposited on flexible substrates over metal electrodes with a sensing area of 1 mm². These sensors show a sensitivity of −56.7 ± 1.3 mV/pH, in a wide pH range of 9 to 5. A proof of concept is also demonstrated using a flexible reference electrode in solid electrolyte with a curved surface. A good balance between the performance parameters (sensitivity), the production costs, and simplicity of the sensors was accomplished, as required for wearable biomedical devices

Sample characterization.

<p>Sample characterization.</p

Correlations between CD25^bright/CD4⁺ aTreg frequencies and immunoblot band numbers.

<p>A–C: Bands recognized in immunoblots of HEp2-cytoplasmic proteins by SLE patients, unaffected relatives and unrelated control subjects. D–F: Bands recognized in immunoblots of HEp2-nuclear proteins. Regression lines represent linear regression.</p

Systematic gating for CD25^bright aTregs.

<p>A. First, in order to specify the upper limit of CD25 staining in conventional T-cells for each sample, the ninety-ninth percentile of PE/Cy5 fluorescence intensity within CD4⁻CD45RO⁻ cells (a population containing no Tregs) was determined as gate A. CD4⁺CD25^bright aTregs in the same sample were then quantified as those CD4⁺ cells that exceeded this value at least three-fold (gate B). B–D. The CD25^bright gate (B) contains high proportions of Foxp3+ cells in samples from SLE patients, unaffected relatives and healthy control subjects. As exemplified by an active SLE patient (panel B), an unaffected relative (panel C) and a healthy control subject (panel D), the CD25^bright gate B defined as 3xMFI(gate A) regularly contained 70–90% Foxp3+ cells within CD4+ lymphocytes when additional samples were stained for the same markers as previously but now in combination with intracellular Foxp3 staining.</p

Group-wise coreferentialities between CD25^bright/CD4+ aTreg frequencies and <i>IL2RA</i> genetic-effects model scores.

<p>A: SLE patients, B: unaffected relatives, C: unrelated controls. Reactivities to cytoplasmic bands are indicated by filled circles, anti-nuclear reactivities by triangles and anti-brain reactivities by crosses.</p

Correlations between aTreg frequencies (CD25^bright/CD4⁺) and quantified SLE-associated specific autoreactive IgG.

<p>A–C: IgG anti-dsDNA. D–F: IgG anti-Sm. Regression lines represent linear regression.</p

Group-wise coreferentialities with aTreg frequencies, in respect to 130 IgG immunoblot reactivities as reference data.

<p>A–C: Coreferentiality between CD25^bright/CD4⁺ aTreg frequencies and IgG anti-HEp2-cytoplasmic immunoblot band numbers. D–F: Coreferentiality between aTreg frequencies and log-transformed IgG anti-Ro60/SSA. G–I: Coreferentiality between aTreg frequencies and log-transformed IgG anti-Sm. Reactivities to cytoplasmic bands are indicated by filled circles, anti-nuclear reactivities by triangles and anti-brain reactivities by crosses.</p

Coreferentialities with CD25^bright/CD4⁺ frequencies.

*<p>Significance according to permutation test (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033992#s4" target="_blank">methods</a>). Effects of bystander coreferentiality were checked for all significant tests (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033992#s4" target="_blank">methods</a>), but rates of simulated bystander data reaching the respective test significance never exceeded 5%.</p

Coreferentialities of the <i>IL2RA</i> model score with other parameters.

*<p>Test for multiple parameters, on maximal absolute coreferentiality |<i>R_C</i>|_max (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033992#s4" target="_blank">methods</a>). Significant tests, except those for multiple SNPs, were also checked for the effect of bystander coreferentiality (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033992#s4" target="_blank">methods</a>), but no such effect was detected.</p