This paper presents a novel experimental case study in which tactile sensation is analyzed as 4-dimensional subjective data consisting of stimulus, participant, evaluation term, and temporal components, using a temporal measurement approach. Specifically, the skin and lotion interaction was evaluated using the Temporal Check-All-That-Apply (TCATA) method. Two practical analysis examples were conducted to experimentally demonstrate the potential use cases of time-series subjective tactile data. In the first example, stimulus classification accuracy was compared between different sampling periods, including the whole and late periods, with the latter being akin to the conventional Semantic Differential (SD) method condition. The results indicate that the whole and early periods exhibit higher accuracy compared to the latest period, implying that temporal measurements may capture more stimulus characteristics than the conventional approach. In the second example, cluster analysis based on the time-series subjective data was conducted. The results revealed that the participants were classified into two distinct clusters, with the trends of time-series changes being significantly different between the clusters.

We tested the reliability of sensory evaluations of tactile sensation on bare skin and investigated the reliability among evaluation attributes by trained and untrained assessors. Two trained professional panelists and two untrained researchers evaluated skin in terms of several attributes: smooth–rough, elastic–not elastic, soft–hard (surface), soft–hard (base), moisturized–dry. Twenty-two women aged 25–57 years were evaluated, and the sensory evaluation was repeated twice. Correlation coefficients and intraclass correlation coefficients (ICCs) were used to examine intra- and inter-assessor reliability. The sensory evaluation and physical quantities acquired by commercial and non-commercial instruments were moderately correlated. Smooth–rough and elastic–not elastic showed high or moderate inter-assessor reliabilities with mean correlation coefficients between panelists of 0.81 and 0.58, respectively. Further, the ICC (2,1) values were 0.64 and 0.51, respectively, and the ICC (2,2) values were 0.77 and 0.67, respectively. Conversely, the reliabilities of soft–hard (surface), soft–hard (base), and moisturized–dry were low; the mean correlation coefficients between the panelists were 0.36, 0.23, and 0.22; the ICC (2,1) values were 0.27, 0.23, and 0.17; and the ICC (2,2) values were 0.42, 0.29, and 0.26, respectively. Reliability differed between attributes. We found no meaningful differences between the trained and untrained panelists regarding intra- or inter-assessor reliability.

The quantification of stickiness experienced upon touching a sticky or adhesive substance has attracted intense research attention, particularly for application to haptics, virtual reality, and human–computer interactions. Here, we develop and evaluate a device that quantifies the feeling of stickiness experienced upon touching an adhesive substance. Keeping in mind that a typical pressure distribution sensor can only measure a pressing force, but not a tensile force, in our setup, we apply an offset pressure to a pressure distribution sensor and measure the tensile force generated by an adhesive substance as the difference from the offset pressure. We propose a method of using a magnetic force to generate the offset pressure and develop a measuring device using a magnet that attracts magnetic pin arrays and pin magnets; the feasibility of the method is verified with a first prototype. We develop a second prototype that overcomes the noise problems of the first, arising from the misalignment of the pins owing to the bending of the magnetic force lines at the sensor edges. We also obtain measurement results for actual samples and standard viscosity liquids. Our findings indicate the feasibility of our setup as a suitable device for measuring stickiness.