Shogo Okamoto

When a human strokes the surface of an object with his/her finger, the surface shape influences the perceived softness of the object. This study introduced a curved surface softness illusion, which alters the perception of material softness. When a surface with curvature is felt by sliding a finger over it, it feels softer than a flat surface made of the same material. In contrast, a rugged surface is perceived as harder. This illusion indicates that, in addition to mechanical hardness, humans judge an object’s softness based on its surface shape.

Gait stability indices are crucial for identifying individuals at risk of falling while walking. The margin of stability is one such index, known for its good construct validity. Generally, the measurement of this stability index requires a motion capture system, rendering it inaccessible for everyday use. This study proposes an alternative approach by estimating the index through time-series data of triaxial kinematic motion from a single body feature. We analyzed an open gait database comprising data from 60 participants aged over 60 to identify the most accurate body feature for estimating the margin of stability. The margin of stability values were estimated by using principal motion analysis, with the time series of the triaxial translational velocities of a body feature as predictors. Among the 10 body feature points, the sacral crest provided the highest accuracy, with the correlation coefficients between observation and estimation being 0.56 and 0.54 for the mediolateral and anterior directions, respectively. Although these values need to be further improved, these findings pave the way for developing an accessible system to estimate fall risks.

Temporal dominance of sensations (TDS) methods are used to record temporally developing sensations while eating food samples. Results of TDS tasks are typically discussed using averages across multiple trials and panels, and few methods have been developed to analyze differences between individual trials. We defined a similarity index between two time-series responses of TDS tasks. This index adopts a dynamic level to determine the importance of the timing of attribute selection. With a small dynamic level, the index focuses on the duration for attributes to be selected rather than on the timing of the attribute selection. With a large dynamic level, the index focuses on the temporal similarity between two TDS tasks. We performed an outlier analysis based on the developed similarity index using the results of TDS tasks performed in an earlier study. Certain samples were categorized as outliers irrespective of the dynamic level, whereas the categorization of a few samples depended on the level. The similarity index developed in this study achieved individual analyses of TDS tasks, including outlier detection, and adds new analysis techniques to TDS methods.