Motion Tracker: Camera-Based Monitoring of Bodily Movements Using Motion Silhouettes

Researchers in the cognitive and affective sciences investigate how thoughts and feelings are reflected in the bodily response systems including peripheral physiology, facial features, and body movements. One specific question along this line of research is how cognition and affect are manifested in the dynamics of general body movements. Progress in this area can be accelerated by inexpensive, non-intrusive, portable, scalable, and easy to calibrate movement tracking systems. Towards this end, this paper presents and validates Motion Tracker, a simple yet effective software program that uses established computer vision techniques to estimate the amount a person moves from a video of the person engaged in a task (available for download from http://jakory.com/motion-tracker/). The system works with any commercially available camera and with existing videos, thereby affording inexpensive, non-intrusive, and potentially portable and scalable estimation of body movement. Strong between-subject correlations were obtained between Motion Tracker’s estimates of movement and body movements recorded from the seat (r =.720) and back (r = .695 for participants with higher back movement) of a chair affixed with pressure-sensors while completing a 32-minute computerized task (Study 1). Within-subject cross-correlations were also strong for both the seat (r =.606) and back (r = .507). In Study 2, between-subject correlations between Motion Tracker’s movement estimates and movements recorded from an accelerometer worn on the wrist were also strong (rs = .801, .679, and .681) while people performed three brief actions (e.g., waving). Finally, in Study 3 the within-subject cross-correlation was high (r = .855) when Motion Tracker’s estimates were correlated with the movement of a person’s head as tracked with a Kinect while the person was seated at a desk (Study 3). Best-practice recommendations, limitations, and planned extensions of the system are discussed.     

Cell Physician: Reading Cell Motion

Cell motility is an essential phenomenon in almost all living organisms. It is natural to think that behavioral or shape changes of a cell bear information about the underlying mechanisms that generate these changes. Reading cell motion, namely, understanding the underlying biophysical and mechanochemical processes, is of paramount importance. The mathematical model developed in this paper determines some physical features and material properties of the cells locally through analysis of live cell image sequences and uses this information to make further inferences about the molecular structures, dynamics, and processes within the cells, such as the actin network, microdomains, chemotaxis, adhesion, and retrograde flow. The generality of the principals used in formation of the model ensures its wide applicability to different phenomena at various levels. Based on the model outcomes, we hypothesize a novel biological model for collective biomechanical and molecular mechanism of cell motion.     

Enhancement of Glossiness Perception by Retinal-Image Motion: Additional Effect of Head-Yoked Motion Parallax

It has been argued that when an observer moves, a contingent retinal-image motion of a stimulus would strengthen the perceived glossiness. This would be attributed to the veridical perception of three-dimensional structure by motion parallax. However, it has not been investigated whether the effect of motion parallax is more than that of retinal-image motion of the stimulus. Using a magnitude estimation method, we examine in this paper whether cross-modal coordination of the stimulus change and the observer''s motion (i.e., motion parallax) is essential or the retinal-image motion alone is sufficient for enhancing the perceived glossiness. Our data show that a retinal-image motion simulating motion parallax without head motion strengthened the perceived glossiness but that its effect was weaker than that of motion parallax with head motion. These results suggest the existence of an additional effect of the cross-modal coordination between vision and proprioception on glossiness perception. That is, motion parallax enhances the perception of glossiness, in addition to retinal-image motions of specular surfaces.     

Patterns of Change: Forces and Motion

Patterns of Change: Forces and Motion is an integrated science lesson that uses the 5E lesson cycle to tie together science with language arts, mathematics, literature, technology, engineering and social studies in an engaging format applicable for young learners. This lesson has been uniquely designed for the purpose of providing elementary teachers with ideas for using hands-on minds-on activities to foster inquiry and discussion, while engaging their students to use technology as a learning tool. This lesson has been used on the elementary level to teach students about the forces that have an effect on motion.     

Motion perception: from phi to omega.

When human observers view dynamic random noise, such as television ''snow'', through a curved or annular aperture, they experience a compelling illusion that the noise is moving smoothly and coherently around the curve (the ''omega effect''). In several series of experiments, we have investigated the conditions under which this effect occurs and the possible mechanisms that might cause it. We contrast the omega effect with ''phi motion'', seen when an object suddenly changes position. Our conclusions are that the visual scene is first segmented into objects before a coherent velocity is assigned to the texture on each object''s surface. The omega effect arises because there are motion mechanisms that deal specifically with object rotation and these interact with pattern mechanisms sensitive to curvature.     

Motion: the long and short of it

Several authors have proposed that motion is analyzed by two separate processes: short-range and long-range. We claim that the differences between short-range and long-range motion phenomena are a direct consequence of the stimuli used in the two paradigms and are not evidence for the existence of two qualitatively different motion processes. We propose that a single style of motion analysis, similar to the well known Reichardt and Marr-Ullman motion detectors, underlies all motion phenomena. Although there are different detectors of this type specialized for different visual attributes (namely first-order and second-order stimuli), they all share the same mode of operation. We review the studies of second-order motion stimuli to show that they share the basic phenomena observed for first-order stimuli. The similarity across stimulus types suggests, not parallel streams of motion extraction, one short-range and passive and the other long-range and intelligent, but a concatenation of a common mode of initial motion extraction followed by a general inference process.