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Innovative Sensing Methods for Motion and Behavior Analysis

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 641

Special Issue Editors


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Guest Editor
Graduate School of Information Sciences, Tohoku University, Sendai, Japan
Interests: visual servo; image processing; robot control and system biology

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Guest Editor
Graduate School of Information Sciences, Tohoku University, Sendai, Japan
Interests: light transport on projector-camera system; point cloud processing; application of point cloud processing methods to biochemistry

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Guest Editor
Graduate School of Medicine, Tohoku University, Sendai, Miyagi 980-8575, Japan
Interests: RFID; animal tracking; autism spectrum disorder; multi-animal behavioral analysis; behavioral neuroscience

Special Issue Information

Dear Colleagues,

This Special Issue of Sensors, entitled Innovative Sensing Methods for Motion and Behavior Analysis, will bring together cutting-edge research on sensor technologies and methodologies for capturing and interpreting dynamic behaviors in humans and animals. With the proliferation of advanced sensing modalities—ranging from optical systems and high-speed cameras to ultrasonic sensors, RFID tags, GPS modules, and hyperspectral imaging—novel approaches for motion tracking, localization, and behavioral inference are emerging across disciplines. We welcome original articles and comprehensive reviews on topics such as simultaneous localization and mapping (SLAM); behavior digitalization by multiple cameras, body cameras, eye-glasses, and UAV cameras with deep-learning algorithms; sensor fusion techniques; vision-based tracking under challenging conditions; and bio-inspired sensing systems. Particular emphasis will be placed on dynamic, time-dependent behaviors and human–object or animal–environment interactions. Contributions exploring applications in navigation, behavioral neuroscience, sports analytics, animal ecology, robotics, and smart environments are highly encouraged. We also invite submissions on emerging fields such as wearable inertial measurement units (IMUs), soft sensors for biomechanical monitoring, and sensor-based multimodal data analytics for behavioral modeling. This Special Issue aligns with the scope of Sensors by addressing both the development of innovative sensor solutions and their applications in real-world motion and behavior analysis.

Prof. Dr. Koichi Hashimoto
Dr. Naoya Chiba
Dr. Shohei Ochi
Guest Editors

Manuscript Submission Information

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Keywords

  • motion analysis
  • behavior sensing
  • sensor fusion
  • SLAM
  • hyperspectral imaging
  • RFID
  • high-speed vision
  • animal tracking
  • human–machine interaction

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Published Papers (1 paper)

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Research

32 pages, 37329 KB  
Article
Movement Artifact Direction Estimation Based on Signal Processing Analysis of Single-Frame Images
by Woottichai Nonsakhoo and Saiyan Saiyod
Sensors 2025, 25(24), 7487; https://doi.org/10.3390/s25247487 - 9 Dec 2025
Viewed by 447
Abstract
Movement artifact direction and magnitude are critical parameters in noise detection and image analysis, especially for single-frame images where temporal information is unavailable. This paper introduces the Movement Artifact Direction Estimation (MADE) algorithm, a signal processing-based approach that performs 3D geometric analysis to [...] Read more.
Movement artifact direction and magnitude are critical parameters in noise detection and image analysis, especially for single-frame images where temporal information is unavailable. This paper introduces the Movement Artifact Direction Estimation (MADE) algorithm, a signal processing-based approach that performs 3D geometric analysis to estimate both the direction (in degrees) and weighted quantity (in pixels) of movement artifacts. Motivated by computational challenges in medical image quality assessment systems such as LUIAS, this work investigates directional multiplicative noise characterization using controlled experimental conditions with optical camera imaging. The MADE algorithm operates on multi-directional quantification outputs from a preprocessing pipeline—MAPE, ROPE, and MAQ. The methodology is designed for computational efficiency and instantaneous processing, providing interpretable outputs. Experimental results using precision-controlled apparatus demonstrate robust estimation of movement artifact direction and magnitude across a range of image shapes and velocities, with principal outputs aligning closely to ground truth parameters. The proposed MADE algorithm offers a methodological proof of concept for movement artifact analysis in single-frame images, emphasizing both directional accuracy and quantitative assessment under controlled imaging conditions. Full article
(This article belongs to the Special Issue Innovative Sensing Methods for Motion and Behavior Analysis)
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