Gait Recognition and Digital Mobility Outcomes Based on Wearable Sensing Technology
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 506
Special Issue Editor
Interests: technology in motion; gait-environment interactions; visual computing; augmented reality; walking assessments
Special Issue Information
Dear Colleagues,
Wearable sensors can provide continuous and objective measurements of human motion, enabling gait recognition (alongside other motor states) and its quantification with digital mobility outcomes in controlled and real-world settings. Advances in wearable sensor technology and their increased public accessibility (e.g., phones, smart glasses, AR/VR), in combination with advances in signal processing, sensor fusion, machine learning and AI, now allow us more than ever to extract meaningful metrics for gait and mobility activity, performance and quality. These metrics may inform various applications in sports, health care and activity monitoring.
This Special Issue of Sensors welcomes contributions focused on methodological innovations and data-driven approaches for detecting and analysing gait and mobility using wearable technologies. Topics include motor state detection, signal denoising, event detection, machine learning for mobility metrics, and validation of wearable-based mobility metrics with (ideally) direct practical implications for use in applied settings. By integrating expertise from engineering, data science, movement science, and health technology into applied fields like sports, rehabilitation and lifestyle interventions, this Special Issue aims to advance quantitative methods that transform wearable sensor data of gait into actionable digital mobility outcomes for real-world use cases.
Dr. Melvyn Roerdink
Guest Editor
Mr. Pieter van Doorn
Guest Editor Assistant
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Keywords
- wearable sensors
- gait analysis
- digital mobility outcomes
- signal processing
- data fusion
- machine learning
- mobility metrics
- real-world monitoring
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