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Innovative Sensing Methods for Motion and Behavior Analysis

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

Deadline for manuscript submissions: 20 July 2026 | Viewed by 6077

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Special Issue Editors

Prof. Dr. Koichi Hashimoto

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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

Dr. Naoya Chiba

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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

Dr. Shohei Ochi

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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

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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 (7 papers)

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Research

16 pages, 3108 KB

Open AccessArticle

A Biomechanical Analysis of Two-Person Emergency Patient Lifting Techniques Using Motion Capture and Ergonomic Assessment

by Xiaoxu Ji, Xin Gao, Paige L. Johnson and Isaac Wheeler

Sensors 2026, 26(9), 2747; https://doi.org/10.3390/s26092747 - 29 Apr 2026

Viewed by 346

Abstract

Emergency responders face a high risk of musculoskeletal disorders (MSDs), particularly lower back injuries, due to frequent patient-handling tasks performed in awkward and dynamic postures. This aim of study is to utilize dual motion capture systems integrated with a digital human modeling (DHM) ergonomics tool to evaluate the biomechanical effects of two common two-person carrying techniques: facing forward and facing each other. Twenty-two participants lifted a 25 kg mannequin while wearing Xsens motion sensors, and lumbar forces and joint angles were analyzed using Siemens Jack software (v9.0). Peak compressive and anterior–posterior (AP) shear forces, along with trunk, hip, and knee joint angles, were examined. Compressive forces ranged from approximately 948.6 to 2955.6 N, and AP shear forces ranged from 286.0 to 827.0 N. Mean compressive and AP shear forces were higher during the facing-each-other task (1977.3 N and 595.0 N) than during the facing-forward task (1596.0 N and 462.0 N). Males experienced higher spinal loads than females across both tasks. The facing-each-other technique was associated with greater hip flexion, lower knee flexion, and reduced trunk flexion, whereas the facing-forward technique resulted in less hip flexion, greater knee flexion, and greater trunk flexion. Overall, under the conditions of the present study, the facing-forward technique was associated with lower lumbar loading indicators. Integrating motion capture with DHM offers a valuable approach for evaluating realistic rescue tasks and can inform ergonomic training strategies for emergency responders. Full article

(This article belongs to the Special Issue Innovative Sensing Methods for Motion and Behavior Analysis)

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51 pages, 55716 KB

Open AccessArticle

A Novel Method for Motion Blur Detection and Quantification Using Signal Analysis on a Controlled Empirical Image Dataset

by Woottichai Nonsakhoo and Saiyan Saiyod

Sensors 2026, 26(8), 2360; https://doi.org/10.3390/s26082360 - 11 Apr 2026

Viewed by 357

Abstract

Motion blur degrades single-frame imaging when relative motion occurs during sensor exposure; yet, quantitative validation is difficult because ground-truth motion parameters are rarely available in real images. This paper presents an interpretable, measure-first framework for detecting, localizing, and quantifying motion blur in single-frame grayscale images under a validated operating condition of one-dimensional horizontal uniform motion. The method analyzes each image row as a one-dimensional spatial signal, where Movement Artifact denotes the scanline-level imprint of motion blur retained in the legacy algorithm names MAPE and MAQ. The pipeline combines three stages: Movement Artifact Position Estimation (MAPE) using scanline self-similarity, Reference Origin Point Estimation (ROPE) using robust structural trends, and Movement Artifact Quantification (MAQ), which summarizes blur magnitude as an average horizontal spatial displacement after adaptive filtering. The pipeline is evaluated on a controlled empirical dataset of 110 images of a high-contrast marker acquired at known tangential velocities from 0.0 to 1.0 m/s in 0.1 m/s increments (10 images per level). MAPE achieves 70–90% detection rates across velocities, and ROPE localizes reference origins with 97–99% detection. An empirical polynomial mapping from MAQ to velocity attains R² = 0.9900 with RMSE 0.0229 m/s and MAE 0.0221 m/s over 0.0–0.7 m/s, enabling calibrated velocity estimates from blur measurements within the validated regime. An extended additive-noise robustness analysis further shows that severe perturbation can preserve candidate self-similarity responses while progressively destabilizing reference-origin localization and MAQ pairing, thereby clarifying the empirical boundary of the current controlled single-marker regime. The approach is not claimed to generalize to uncontrolled scenes, non-uniform blur, or multi-dimensional and non-rigid motion. Full article

(This article belongs to the Special Issue Innovative Sensing Methods for Motion and Behavior Analysis)

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39 pages, 96608 KB

Open AccessArticle

Multi-Modal Feature Fusion and Hierarchical Classification for Automated Equine–Human Interaction Behavior Recognition

by Samierra Arora, Emily Kieson, Christine Rudd and Peter A. Gloor

Sensors 2026, 26(7), 2202; https://doi.org/10.3390/s26072202 - 2 Apr 2026

Viewed by 1773

Abstract

Automated recognition of equine–human interaction behaviors from video represents a significant challenge in computational ethology, with critical applications spanning animal welfare assessment, equine-assisted services evaluation, and safety monitoring in equestrian environments. Existing approaches to animal behavior recognition typically focus on single species in isolation, rely solely on facial expression analysis while ignoring full-body posture, or employ flat classification architectures that fail under the severe class imbalances characteristic of naturalistic behavioral datasets. Furthermore, no prior framework integrates simultaneous analysis of both human and equine body language for cross-species interaction classification. This paper presents a novel hierarchical classification framework integrating multi-modal computer vision features to distinguish behavioral states during horse–human encounters. Our methodology employs three complementary feature extraction pipelines: YOLOv8 for spatial relationship modeling, MediaPipe for human postural analysis, and AP-10K for equine body language interpretation. From 28 annotated interaction videos comprising 50,270 temporal samples across five horse breeds, we extract 35 discriminative features capturing proximity dynamics, body orientation, and species-specific behavioral indicators. To address severe class imbalance (18.3:1 ratio between affiliative and avoidant categories), we implement cost-sensitive gradient boosting with automatic class weight optimization within a two-stage hierarchical architecture. The first stage classifies interactions into three parent categories (affiliative, neutral, avoidant) achieving 73.2% balanced accuracy, while stage two discriminates six fine-grained sub-behaviors achieving 88.5% balanced accuracy (under oracle parent-category routing; cascaded end-to-end performance is 62.9% balanced accuracy due to Stage 1 error propagation, identifying parent classification as the primary bottleneck). Notably, our system achieves 85.0% recall on safety-critical avoidant behaviors despite their representation of only 3.8% of the dataset. Extensive ablation studies demonstrate that equine pose features contribute most critically to classification performance, while comprehensive cross-validation analysis confirms model robustness across diverse interaction contexts. The proposed framework establishes the first systematic multimodal cross-species behavioral assessment pipeline in human–animal interaction research, with direct implications for improving equine welfare monitoring and rider safety protocols. Full article

(This article belongs to the Special Issue Innovative Sensing Methods for Motion and Behavior Analysis)

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19 pages, 1217 KB

Open AccessArticle

Talking with Actionbits—A Part-Enhanced VLM for Action and Interaction Recognition in Animals

by Yang Yang, Ren Nakagawa, Risa Shinoda, Hiroaki Santo, Kenji Oyama, Takenao Ohkawa and Fumio Okura

Sensors 2026, 26(6), 1969; https://doi.org/10.3390/s26061969 - 21 Mar 2026

Viewed by 494

Abstract

Understanding animal actions and interactions is essential for behavior analysis and ecological monitoring. Although large-scale in-the-wild datasets have advanced animal action recognition, existing methods still struggle with fine-grained motion, spatial relations, and multi-individual interactions. To address these challenges, we introduce AIRA, a unified framework for Action and Interaction Recognition in Animals. Built upon a vision–language model (VLM), AIRA learns in an action-centered representation space defined by body parts and their corresponding motions, thereby improving robustness to background noise and enabling cross-species generalization via a unified mammal-centric part ontology. To model actions, we treat body parts and motion as primary cues and introduce Actionbit tokens—compact representations for parts and motions generated by a large language model (LLM) that encode which parts move and how. We further propose Part-Enhanced Prompt Fine-tuning (PEPF) to make the VLM explicitly sensitive to part and pose cues. Within PEPF, the Action–actionbit Alignment (AbA) module enriches action representations with fine-grained part–motion semantics, and Part-Vision Prompting (PVP) extracts keyframes through action-aware prompting. Experiments across multiple benchmarks show consistent improvements in both action and interaction recognition, highlighting the importance of action-centered adaptation and relational reasoning for understanding animal behavior in the wild. Full article

(This article belongs to the Special Issue Innovative Sensing Methods for Motion and Behavior Analysis)

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16 pages, 1100 KB

Open AccessArticle

Balance Assessments Using Smartphone Sensor Systems and a Clinician-Led Modified BESS Test in Soccer Athletes with Hip-Related Pain: An Exploratory Cross-Sectional Study

by Alexander Puyol, Matthew King, Charlotte Ganderton, Shuwen Hu and Oren Tirosh

Sensors 2026, 26(3), 1061; https://doi.org/10.3390/s26031061 - 6 Feb 2026

Viewed by 723

Abstract

Background: The Balance Error Scoring System (BESS) is the most practiced static postural balance assessment tool, which relies on visual observation, and has been adopted as the gold standard in the clinic and field. However, the BESS can lead to missed and inaccurate diagnoses—because of its low inter-rater reliability and limited sensitivity—by missing subtle balance deficits, particularly in the athletic population. Smartphone technology using motion sensors may act as an alternative option for providing quantitative feedback to healthcare clinicians when performing balance assessments. The primary aim of this study was to explore the discriminative validity of an alternative novel smartphone-based cloud system to measure balance remotely in soccer athletes with and without hip pain. Methods: This is an exploratory cross-sectional study. A total of 64 Australian soccer athletes (128 hips, 28% females) between 18 and 40 years completed single and tandem stance balance tests that were scored using the modified BESS test and quantified using the smartphone device attached to their lower back. An Exploratory Factor Analysis (EFA) and a Clustered Receiver Operating Characteristic (ROC) using an Area Under the Curve (AUC) were used to explore the discriminative validity between the smartphone sensor system and the modified BESS test. A Linear Mixed-Effects Analysis of Covariance (ANCOVA) was used to determine any statistical differences in static balance measures between individuals with and without hip-related pain. Results: EFA revealed that the first factor primarily captured variance related to smartphone measurements, while the second factor was associated with modified BESS test scores. The ROC and the AUC showed that the smartphone sway measurements in the anterior–posterior and mediolateral directions during single-leg stance had an acceptable to excellent level of accuracy in distinguishing between individuals with and without hip-related pain (AUC = 0.72–0.80). Linear Mixed-Effects ANCOVA analysis found that individuals with hip-related pain had significantly less single-leg balance variability and magnitude in the anteroposterior and mediolateral directions compared to individuals without hip-related pain (p < 0.05). Conclusion: Due to the ability of smartphone technology to discriminate between individuals with and without hip-related pain during single-leg static balance tasks, it is recommended to use the technology in addition to the modified BESS test to optimise a clinician-led assessment and to further guide clinical balance decision-making. While the study supports smartphone technology as a method to assess static balance, its use in measuring balance during dynamic movements needs further research. Full article

(This article belongs to the Special Issue Innovative Sensing Methods for Motion and Behavior Analysis)

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18 pages, 3369 KB

Open AccessArticle

3D Local Feature Learning and Analysis on Point Cloud Parts via Momentum Contrast

by Xuanmeng Sha, Tomohiro Mashita, Naoya Chiba and Liyun Zhang

Sensors 2026, 26(3), 1007; https://doi.org/10.3390/s26031007 - 3 Feb 2026

Viewed by 626

Abstract

Self-supervised contrastive learning has demonstrated remarkable effectiveness in learning visual representations without labeled data, yet its application to 3D local feature learning from point clouds remains underexplored. Existing methods predominantly focus on complete object shapes, neglecting the critical challenge of recognizing partial observations commonly encountered in real-world 3D perception. We propose a momentum contrastive learning framework specifically designed to learn discriminative local features from randomly sampled point cloud regions. By adapting the MoCo architecture with PointNet++ as the feature backbone, our method treats local parts of point cloud as fundamental contrastive learning units, combined with carefully designed augmentation strategies including random dropout and translation. Experiments on ShapeNet demonstrate that our approach effectively learns transferable local features and the empirical observation that approximately 30% object local part represents a practical threshold for effective learning when simulating real-world occlusion scenarios, and achieves comparable downstream classification accuracy while reducing training time by 16%. Full article

(This article belongs to the Special Issue Innovative Sensing Methods for Motion and Behavior Analysis)

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32 pages, 37329 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1096

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 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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