The perceived orientation of objects, gravity, and the body are biased to the left. Whether this leftward bias is attributable to biases in sensing or processing vestibular, visual, and body sense cues has never been assessed directly. The orientation in which characters are most easily recognized--the perceived upright (PU)--can be well predicted from a weighted vector sum of these sensory cues. A simple form of this model assumes that the directions of the contributing inputs are coded accurately and as a consequence participants tilted left- or right-side-down relative to gravity should exhibit mirror symmetric patterns of responses. If a left/right asymmetry were present then varying these sensory cues could be used to assess in which sensory modality or modalities a PU bias may have arisen. Participants completed the Oriented Character Recognition Test (OCHART) while manipulating body posture and visual orientation cues relative to gravity. The response patterns showed systematic differences depending on which side they were tilted. An asymmetry of the PU was found to be best modeled by adding a leftward bias of 5.6° to the perceived orientation of the body relative to its actual orientation relative to the head. The asymmetry in the effect of body orientation is reminiscent of the body-defined left-leaning asymmetry in the perceived direction of light coming from above and reports that people tend to adopt a right-leaning posture.
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http://dx.doi.org/10.1167/13.2.3 | DOI Listing |
J Biomech
January 2025
Laboratoire de Simulation et Modélisation du Mouvement, Université de Montréal, Montréal, QC, Canada. Electronic address:
In trampolining, optimizing body orientation during landing reduces injury risk and enhances performance. As trampolinists are subject to motor variability, anticipatory inflight corrections are necessary to regulate their body orientation before landing. We investigated the evolution of a) body orientation and b) limb position (i.
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College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China.
This paper aims to address the challenge of precise robotic grasping of molecular sieve drying bags during automated packaging by proposing a six-dimensional (6D) pose estimation method based on an red green blue-depth (RGB-D) camera. The method consists of three components: point cloud pre-segmentation, target extraction, and pose estimation. A minimum bounding box-based pre-segmentation method was designed to minimize the impact of packaging wrinkles and skirt curling.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Cognitive Systems Lab, University of Bremen, 28359 Bremen, Germany.
This paper presents an approach for event recognition in sequential images using human body part features and their surrounding context. Key body points were approximated to track and monitor their presence in complex scenarios. Various feature descriptors, including MSER (Maximally Stable Extremal Regions), SURF (Speeded-Up Robust Features), distance transform, and DOF (Degrees of Freedom), were applied to skeleton points, while BRIEF (Binary Robust Independent Elementary Features), HOG (Histogram of Oriented Gradients), FAST (Features from Accelerated Segment Test), and Optical Flow were used on silhouettes or full-body points to capture both geometric and motion-based features.
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Clinical Laboratory of Radiology and Medical Imaging, "Sf. Apostol Andrei" County Emergency Hospital, 900591 Constanta, Romania.
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View Article and Find Full Text PDFPLoS One
January 2025
Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon.
In human activity-recognition scenarios, including head and entire body pose and orientations, recognizing the pose and direction of a pedestrian is considered a complex problem. A person may be traveling in one sideway while focusing his attention on another side. It is occasionally desirable to analyze such orientation estimates using computer-vision tools for automated analysis of pedestrian behavior and intention.
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