Sensing Motion Decoding Behavior: Sensor-Driven Machine Learning for Next-Generation Gait Analysis
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 11
Special Issue Editors
Interests: gait recognition; machine learning; image processing
Special Issue Information
Dear Colleagues,
Gait analysis, the study of human walking patterns, has emerged as a powerful modality for understanding identity, intent, and health. Fueled by advances in sensor technologies and machine learning, gait-based research is rapidly expanding beyond traditional vision-based settings into diverse sensor modalities and real-world applications.
This Special Issue aims to gather cutting-edge research on sensor-driven gait analysis, encompassing a wide spectrum of sensing platforms, including wearable devices (e.g., IMUs, pressure insoles, and EMG) and contactless systems (e.g., radar, LiDAR, Wi-Fi, infrared, and RGB/depth/thermal cameras). These technologies offer varying trade-offs in accuracy, intrusiveness, scalability, and privacy, opening new possibilities in both controlled and unconstrained environments.
We invite the submission of original contributions that push the boundaries of sensor design, multimodal learning, and intelligent inference for gait-related tasks. Topics may include biometric recognition, soft biometric estimation, behavioral understanding, anomaly detection, and health monitoring. By promoting interdisciplinary dialog across computer vision, pattern recognition, biomedical engineering, and ubiquitous computing, this Special Issue seeks to inspire next-generation innovations in gait analysis and advance the field toward broader deployment and impact.
Topics of interest include (but are not limited to) the following:
- Gait-based identification, verification, and authentication;
- Soft biometric estimation from gait (e.g., age, gender, height, and health status);
- Gait analysis using wearable sensors (IMU, pressure insoles, and EMG);
- Contactless gait measurement using radar, LiDAR, Wi-Fi, or infrared sensors;
- Vision-based gait analysis using RGB, depth, or thermal imaging;
- Multimodal sensor fusion and cross-sensor domain adaptation;
- Gait analysis under unconstrained or in-the-wild conditions;
- Dataset creation, benchmarking, and evaluation protocols for gait-related tasks;
- Gait-based applications in healthcare, rehabilitation, emotion recognition, and security;
- The integration of gait features in person re-identification tasks.
Dr. Chi Xu
Dr. Xiang Li
Guest Editors
Manuscript Submission Information
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Keywords
- gait recognition
- sensor-based human sensing
- soft biometrics
- multimodal machine learning
- human motion understanding
- wearable and contactless sensing
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