Humans and other animals can measure distances nonvisually by legged locomotion. Experiments typically employ an outbound measure (M) and an inbound report (R) phase. Previous research has found distance reproduction to be maximally accurate, when gait symmetry and speed of M and R are of like kind: Successful human odometry manifests at the level of the M-R system. In the present work, M was an experimenter-set distance produced by a blindfolded participant using a primary gait (walk, run). R was always by walk. Fast and slow versions of walk and run were adopted by participants, such that when M was fast R was slow, and vice versa. Distance was underestimated when M was slower than R and overestimated when M was faster than R. However, the pattern of participant-adopted velocities indicated that it was the instructions, not the speed as such, that yielded the pattern of results. The results are interpretable through a dynamical perspective and indicate speed is an imperfection parameter acting on the attractors of the M-R system.
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http://dx.doi.org/10.1080/00222895.2011.642026 | DOI Listing |
Sensors (Basel)
November 2024
School of Computer Science and Technology, North University of China, Taiyuan 030051, China.
Simultaneous Localization And Mapping (SLAM) algorithms play a critical role in autonomous exploration tasks requiring mobile robots to autonomously explore and gather information in unknown or hazardous environments where human access may be difficult or dangerous. However, due to the resource-constrained nature of mobile robots, they are hindered from performing long-term and large-scale tasks. In this paper, we propose an efficient multi-robot dense SLAM system that utilizes a centralized structure to alleviate the computational and memory burdens on the agents (i.
View Article and Find Full Text PDFPsychol Res
November 2024
Department of Biomechanics, University of Nebraska at Omaha, 6160 University Dr S, Omaha, NE, 68182, USA.
Human odometry refers to an individual's ability to travel between locations without eyesight and without designating a conscious effort toward spatially updating themselves as they travel through the environment. A systematic review on human odometry was completed for the purpose of establishing the state-of-the-art of the topic, and based on this information, develop meaningful hypotheses using Strong Inference. The following databases were searched up to February 16, 2023, and accessed through University of Nebraska at Omaha proxied databases: IEEEXplore, PsycArticles, PsycInfo, PubMed Central, SCOPUS, and Web of Science.
View Article and Find Full Text PDFWe introduce the Visual Experience Dataset (VEDB), a compilation of more than 240 hours of egocentric video combined with gaze- and head-tracking data that offer an unprecedented view of the visual world as experienced by human observers. The dataset consists of 717 sessions, recorded by 56 observers ranging from 7 to 46 years of age. This article outlines the data collection, processing, and labeling protocols undertaken to ensure a representative sample and discusses the potential sources of error or bias within the dataset.
View Article and Find Full Text PDFBehav Res Methods
April 2024
Laboratory for Experimental Ophthalmology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Eye movements offer valuable insights for clinical interventions, diagnostics, and understanding visual perception. The process usually involves recording a participant's eye movements and analyzing them in terms of various gaze events. Manual identification of these events is extremely time-consuming.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
November 2023
This research introduces a novel, highly precise, and learning-free approach to locomotion mode prediction, a technique with potential for broad applications in the field of lower-limb wearable robotics. This study represents the pioneering effort to amalgamate 3D reconstruction and Visual-Inertial Odometry (VIO) into a locomotion mode prediction method, which yields robust prediction performance across diverse subjects and terrains, and resilience against various factors including camera view, walking direction, step size, and disturbances from moving obstacles without the need of parameter adjustments. The proposed Depth-enhanced Visual-Inertial Odometry (D-VIO) has been meticulously designed to operate within computational constraints of wearable configurations while demonstrating resilience against unpredictable human movements and sparse features.
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