Proprioception Has Limited Influence on Tactile Reference Frame Selection

ABSTRACTPerceiving the spatial location and physical dimensions of objects that we touch is crucial for goal-directed actions. To achieve this, our brain transforms skin-based coordinates into a reference frame by integrating visual and proprioceptive cues, a process known as tactile remapping. In the current study, we examine the role of proprioception in the remapping process when information from the more dominant visual modality is withheld. We developed a new visual-to-touch sensory substitution device and asked participants to perform a spatial localization task in three different arm postures that included posture switches between blocks of trials. We observed that in the absence of visual information novel proprioceptive inputs can be overridden after switching postures. This behavior demonstrates effective top-down modulations of proprioception and points to the unequal contribution of different sensory modalities to tactile remapping.

Frame Registration for Motion Compensation in Imaging Photoplethysmography

Imaging photoplethysmography (iPPG) is an emerging technology used to assess microcirculation and cardiovascular signs by collecting backscattered light from illuminated tissue using optical imaging sensors. An engineering approach is used to evaluate whether a silicone cast of a human palm might be effectively utilized to predict the results of image registration schemes for motion compensation prior to their application on live human tissue. This allows us to establish a performance baseline for each of the algorithms and to isolate performance and noise fluctuations due to the induced motion from the temporally changing physiological signs. A multi-stage evaluation model is developed to qualitatively assess the influence of the region of interest (ROI), system resolution and distance, reference frame selection, and signal normalization on extracted iPPG waveforms from live tissue. We conclude that the application of image registration is able to deliver up to 75% signal-to-noise (SNR) improvement (4.75 to 8.34) over an uncompensated iPPG signal by employing an intensity-based algorithm with a moving reference frame.

The Strategy of Datum Reference Frame Selection Based on Statistical Learning

A datum selection strategy based on statistical learning is proposed. The datum selection is an important part of tolerance specification which is the base of geometric tolerance selection and tolerance principle selection. The problem of datum selection is to deduce the datum reference frame (DRF) based on geometrical, contact, and positioning characteristics. Currently, heuristic rules are used for DRF selection, leading to suboptimal choice of DRF in many cases. The proposed strategy formulates normalized vectors computed from the geometric, contact, and positioning characteristics of surfaces. The surfaces of different parts can be compared by their normalized vectors. Then the statistical learning method is used for building a classifier which can discriminate datum feature vectors based on training samples. Finally, a case study is given to verify the strategy and the different algorithms are compared and discussed.