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This article aimed to identify the effects of a robotic walker on gait dynamics during assisted walking via the analysis of acceleration data. Walking experiments were conducted with 11 healthy young subjects under different conditions, including normal, rollator-assisted, and robotic-walker-assisted walking. We collected acceleration data from the trunk and pelvis to extract measures for evaluating the properties of gait dynamics, including periodicity, complexity, and determinism. These measures include maximum Lyapunov exponent (MLE), multiscale entropy (MSE), multiscale permutation entropy (MPE), correlation dimension (CD), and percentage of determinism (%DET, from recurrence plot). Significant differences in the complex measures, MSE and MPE between normal and walker-assisted walking for trunk acceleration data in anteroposterior (AP) and mediolateral (ML) directions were observed. The determinism measures, CD and %DET, demonstrated significant differences between normal and robotic-walker-assisted walking for trunk acceleration data in the AP direction, while significant differences in the periodicity measures and MLE were not observed between the three conditions. These results imply mostly unchanged local dynamic stability, reduced deterministic patterns, and increased complexity in the gait dynamics of healthy young subjects, especially for the upper body. The robotic walker’s assistance is considered appropriate because of no significant difference between rollator-assisted and robotic-walker-assisted walking. This study provides new insights into the biomechanical effects of the robotic walker on gait. It also suggests that the used methods can be applied to the assistance evaluation of walking assistive devices like lower-limb exoskeletons and assess the ability to resist perturbations with a robotic walker for rehabilitation applications such as perturbation-based gait training. [ABSTRACT FROM AUTHOR]

This study presents a novel concept of robust Kalman filtering, especially dealing with outliers when detecting human hand intrusion using multiple-input–multiple-output radar. Outliers that severely deviate from the normal value for a certain period have not been sufficiently discussed from the viewpoint of safety and can seriously hamper the performance of the Kalman filter. Therefore, two types of outliers were intensively discussed: the temporary outlier that originated in misdetection and the additive outlier that is generated by heavy-tailed error distribution noise. Moreover, an asymmetric outlier processing strategy was proposed depending on positive and negative outliers. The result shows that the proposed method can resist the additive and temporary outliers. [ABSTRACT FROM AUTHOR]

A previous study suggested a relationship between the spatial spectrum of finger pad skin deformation and perception of macroscopic roughness features. This study tested a new hypothesis that macroscopic roughness perception is the result of a weighted linear combination of multiple spatial spectral components of skin deformation. Experiments were conducted by capturing close-up images of finger pad deformation while the pads were pushed onto specimens with macroscopic features. Additionally, the roughness perceptions of these specimens were collected using a magnitude estimation method. The combination of spectral components predicted the roughness perception more accurately than any single spectral component. This suggests that roughness perception is mediated by multiple Gabor filter-like neural systems with different spatial periods, such as visual perception. [ABSTRACT FROM AUTHOR]

Temporal dominance (TD) methods can be used to record temporal changes in multiple sensory and affective responses. TD methods have found wide applications in the analysis of eating experiences of humans. However, extant analyses performed on TD data do not fully utilize the time-series properties of such data. The present study validates the prospect of principal motion analysis (PMA) of TD data. PMA is an extension of principal component analysis, and can be used to resolve multivariate motion data into base principal motions. In this study, panelists were asked to evaluate the tastes of ten types of pickled plums using the temporal dominance of sensations (TDS) and emotions (TDE) methods. Additionally, the panelists were asked to rate the plums using the semantic differential method. Results obtained using both methods were observed to demonstrate good agreement with each other in terms of the structures of reduced variable spaces. As realized in this article, implementation of the combined TD–PMA approach can potentially facilitate statistical discrimination of all food products, whereas conventional methods, such as principal component analysis of data provided via use of the semantic differential method, can at best discriminate only 67 percent of product pairs. PMA can, therefore, be considered as a suitable technique to reveal the characteristics of TD data. [ABSTRACT FROM AUTHOR]

Human perceptual and affective responses change dynamically when stimuli are experienced. In this study, we developed a method for modeling the causal structures of affective dynamics using time-series data. Using the temporal dominance of sensations method, perceptual and affective data were collected from individuals eating strawberries, and the resulting time-series data were mathematically represented using a vector auto-regression model. Multihierarchical and multidimensional causality structures that explain the temporal evolution of perceptual and affective responses were then established based on Granger causality and the information criterion. The established model suggests how affective and preferential responses are triggered following exposure to stimuli. We also assessed the quantitative and semantic validity of the model. [ABSTRACT FROM AUTHOR]