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Advancing Human Gait Monitoring with Wearable Sensors
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Advancing Human Gait Monitoring with Wearable Sensors

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

Deadline for manuscript submissions: 20 September 2026 | Viewed by 6407

Special Issue Editors

Dr. M. Encarna Micó-Amigo

E-Mail Website
Guest Editor
Institute of Mechanical, Process and Energy Engineering, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK
Interests: gait analysis and mobility assessment; wearable sensors for health monitoring; Parkinson’s disease and other movement disorders; digital biomarkers for activities of daily living; objective assessment of motor function; digital health technologies for clinical translation
Dr. Sauvik Das Gupta

E-Mail Website
Guest Editor Assistant
Personalized and Standardized Intervention Based Operation Research Group (PSIBORG), Research Institute on Human and Societal Augmentation (RIHSA) & Behavior Optimization Research Team (BORT), Self-Care Technology (SCT) Integrated Research Center, Department of Information Technology and Human Factors (ITH), National Institute of Advanced Industrial Science and Technology, Kashiwa 277-0882, Japan
Interests: daily-life gait assessment; walking energetics; elderly gait; fall prevention and modeling; Inertial Measurement Units (IMUs) based gait monitoring and analysis; marker-less (video-based) motion capture and analysis; usage of Virtual Reality (VR) in gait analysis; exercise promotion and research translation for societal impact through academic startups

Special Issue Information

Dear Colleagues,

Human gait is a fundamental indicator of health, functional mobility, and neurological status. Advances in wearable sensor technology have enabled continuous, non-invasive, and ecologically valid monitoring of gait in real-world settings. This Special Issue of Sensors aims to explore the latest innovations in

  • Wearable devices;
  • Data acquisition;
  • Signal processing;
  • Machine learning techniques for gait assessment.

Contributions may include, but are not limited to,

  • Inertial measurement units;
  • Pressure insoles;
  • Smart textiles;
  • Multi-sensor integration approaches that capture spatiotemporal, kinematic, kinetic and physiological parameters of human movement;
  • Gait analysis in movement disorders and other clinical conditions.

The Special Issue seeks to highlight research that bridges laboratory-based analyses and monitoring of daily life, with applications in clinical assessment, rehabilitation, fall-risk prediction, sports science, and occupational health. Emphasis is placed on studies that address sensor placement, data reliability, algorithm development, personalization, and the translation of sensor outputs into actionable insights for clinicians, therapists, and individuals.

By focusing on wearable sensors, this Special Issue aligns directly with the journal Sensors, which prioritizes interdisciplinary research on sensor technology, signal processing, and data interpretation. The Special Issue provides a platform for disseminating novel methodologies, practical applications, and challenges in human gait monitoring, fostering the adoption of wearable sensor solutions in healthcare and beyond.

Dr. M. Encarna Micó-Amigo
Guest Editor

Dr. Sauvik Das Gupta
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wearable sensors
  • human gait monitoring
  • inertial measurement units (IMUs)
  • mobility assessment
  • free-living monitoring
  • machine learning
  • digital biomarkers
  • clinical applications
  • rehabilitation technology
  • remote health monitoring

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

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21 pages, 1292 KB  
Article
Motor-Derived Digital Biomarkers for Identifying Low-MoCA Status in People with Parkinson’s Disease
by Bohyun Kim, Changhong Youm, Sang-Myung Cheon, Hwayoung Park, Hyejin Choi, Juseon Hwang and Minsoo Kim
Sensors 2026, 26(8), 2503; https://doi.org/10.3390/s26082503 - 18 Apr 2026
Viewed by 499
Abstract
Cognitive impairment is a prevalent non-motor manifestation of Parkinson’s disease (PD), yet early detection remains limited by the sensitivity of conventional cognitive assessments. Emerging evidence suggests that motor dysfunction, particularly gait and balance abnormalities, reflects underlying cognitive vulnerability. This study examined motor–cognitive associations [...] Read more.
Cognitive impairment is a prevalent non-motor manifestation of Parkinson’s disease (PD), yet early detection remains limited by the sensitivity of conventional cognitive assessments. Emerging evidence suggests that motor dysfunction, particularly gait and balance abnormalities, reflects underlying cognitive vulnerability. This study examined motor–cognitive associations and evaluated whether motor-derived features can be used to classify low-MoCA status in PD without direct cognitive testing. Data from 102 individuals with PD were analyzed, incorporating clinical assessments, physical function measures, lifestyle factors, and gait-derived biomarkers. Multiple regression identified Unified Parkinson’s Disease Rating Scale Part III, stride length of the more affected side during 360° turning at preferred speed, and maximum ankle jerk on the less affected side during forward walking as independent predictors of Montreal Cognitive Assessment scores, collectively explaining 34.7% of the variance. Network analysis revealed integrative relationships among global motor severity, gait smoothness, and cognitive performance. Using a compact motor-based feature set, logistic regression achieved a mean accuracy of 65.8% and an AUC of 0.737 in classifying low-MoCA status under cross-validation. These findings demonstrate that motor-derived digital biomarkers capture clinically meaningful information about cognitive status in PD and may serve as adjunctive tools for identifying cognitive vulnerability in clinical settings. Full article
(This article belongs to the Special Issue Advancing Human Gait Monitoring with Wearable Sensors)
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13 pages, 1073 KB  
Article
Deep Learning for Freezing of Gait Detection: Cross-Dataset Validation Reveals Critical Deployment Gaps Between Laboratory and Daily Living Wearable Monitoring
by Wei Lin and Sanjeet S. Grewal
Sensors 2026, 26(4), 1352; https://doi.org/10.3390/s26041352 - 20 Feb 2026
Viewed by 617
Abstract
Freezing of gait (FoG) affects 38–65% of advanced Parkinson’s disease patients, yet automated detection algorithms are often validated solely on laboratory datasets. This study quantifies the critical performance gap between laboratory and real-world performance—a prerequisite for clinical deployment. Using temporal convolutional networks (TCNs), [...] Read more.
Freezing of gait (FoG) affects 38–65% of advanced Parkinson’s disease patients, yet automated detection algorithms are often validated solely on laboratory datasets. This study quantifies the critical performance gap between laboratory and real-world performance—a prerequisite for clinical deployment. Using temporal convolutional networks (TCNs), we trained models on two public datasets representing ecological extremes: a daily living dataset (Figshare; n = 35, single-sensor) and a laboratory dataset (DAPHNET; n = 10, multi-sensor). We compared five training configurations to address class imbalance. Results showed that F1-based early stopping outperformed Area Under the Curve (AUC)-based stopping by 47% (F1: 0.55 vs. 0.37, p = 0.0008). Combining multiple imbalance corrections (focal loss, weighting, sampling) paradoxically degraded precision to 33% due to a ~60-fold over-weighting of the minority class. Most importantly, cross-dataset validation revealed an 83% performance gap: laboratory F1 reached 0.9999 ± 0.0002, whereas daily living F1 dropped to 0.55 ± 0.26 (p < 0.0001), with a 1299-fold increase in variance. These findings demonstrate that laboratory success does not guarantee real-world utility. We propose that the observed gap represents a “deployment gap” reflecting the combined influence of environmental complexity, sensor constraints, and physiological variability. These results provide an empirical framework for evaluating deployment readiness of wearable FoG detection systems and offer concrete training strategy recommendations for clinical translation. Full article
(This article belongs to the Special Issue Advancing Human Gait Monitoring with Wearable Sensors)
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16 pages, 2262 KB  
Article
Neural Network-Based Granular Activity Recognition from Accelerometers: Assessing Generalizability Across Diverse Mobility Profiles
by Metin Bicer, James Pope, Lynn Rochester, Silvia Del Din and Lisa Alcock
Sensors 2026, 26(4), 1320; https://doi.org/10.3390/s26041320 - 18 Feb 2026
Viewed by 547
Abstract
Human activity recognition (HAR) lies at the core of digital healthcare applications that monitor different types of physical activity. Traditional HAR methods often struggle to adapt to variable-length, real-world activity data and to generalise across cohorts (e.g., from young to old cohorts). Thus, [...] Read more.
Human activity recognition (HAR) lies at the core of digital healthcare applications that monitor different types of physical activity. Traditional HAR methods often struggle to adapt to variable-length, real-world activity data and to generalise across cohorts (e.g., from young to old cohorts). Thus, the aim of this study was to investigate HAR using wearable sensor data, with a particular focus on cross-cohort evaluation. Each dataset included two accelerometers (right thigh and lower back) sampling at 50 Hz, capturing a range of daily-life activities that were annotated using video recordings from chest-mounted cameras synchronised with the accelerometers. Neural networks were trained on young cohorts’ data and tested on old cohorts’ data. The effects of network architecture, sampling frequency and sensor location on classification performance were investigated. Network performance was evaluated using accuracy, recall, precision, F1-score and confusion matrices. The gated recurrent unit architecture achieved the best performance when trained solely on young cohorts’ data, with weighted F1-score of 0.95 ± 0.05 and 0.93 ± 0.05 for young and old cohorts, respectively, resulting in a highly generalizable method. Classification performance across multiple sampling frequencies was comparable. The thigh-mounted sensor consistently achieved higher performance than the lower back sensor across activities except lying. Furthermore, combining datasets significantly improved performance on the old cohort (weighted F1-score: 0.97 ± 0.02) due to increased variability in the training data. This study highlights the importance of network architecture and dataset composition in HAR and demonstrates the potential of neural networks for robust, real-world activity recognition across age-defined cohorts, specifically between young and old cohorts. Full article
(This article belongs to the Special Issue Advancing Human Gait Monitoring with Wearable Sensors)
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15 pages, 4087 KB  
Article
Automatic Identification of Lower-Limb Neuromuscular Activation Patterns During Gait Using a Textile Wearable Multisensor System
by Federica Amitrano, Armando Coccia, Federico Colelli Riano, Gaetano Pagano, Arcangelo Biancardi, Ernesto Losavio and Giovanni D’Addio
Sensors 2026, 26(3), 997; https://doi.org/10.3390/s26030997 - 3 Feb 2026
Viewed by 681
Abstract
Wearable sensing technologies are increasingly used to assess neuromuscular function during daily-life activities. This study presents and evaluates a multisensor wearable system integrating a textile-based surface Electromyography (sEMG) sleeve and a pressure-sensing insole for monitoring Tibialis Anterior (TA) and Gastrocnemius Lateralis (GL) activation [...] Read more.
Wearable sensing technologies are increasingly used to assess neuromuscular function during daily-life activities. This study presents and evaluates a multisensor wearable system integrating a textile-based surface Electromyography (sEMG) sleeve and a pressure-sensing insole for monitoring Tibialis Anterior (TA) and Gastrocnemius Lateralis (GL) activation during gait. Eleven healthy adults performed overground walking trials while synchronised sEMG and plantar pressure signals were collected and processed using a dedicated algorithm for detecting activation intervals across gait cycles. All participants completed the walking protocol without discomfort, and the system provided stable recordings suitable for further analysis. The detected activation patterns showed one to four bursts per gait cycle, with consistent TA activity in terminal swing and GL activity in mid- to terminal stance. Additional short bursts were observed in early stance, pre-swing, and mid-stance depending on the pattern. The area under the sEMG envelope and the temporal features of each burst exhibited both inter- and intra-subject variability, consistent with known physiological modulation of gait-related muscle activity. The results demonstrate the feasibility of the proposed multisensor system for characterising muscle activation during walking. Its comfort, signal quality, and ease of integration encourage further applications in clinical gait assessment and remote monitoring. Future work will focus on system optimisation, simplified donning procedures, and validation in larger cohorts and populations with gait impairments. Full article
(This article belongs to the Special Issue Advancing Human Gait Monitoring with Wearable Sensors)
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Review

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27 pages, 4351 KB  
Review
Wearable Sensor Technologies and Gait Analysis for Early Detection of Dementia: Trends and Future Directions
by Anna Tsiakiri, Spyridon Plakias, Georgios Giarmatzis, Georgia Tsakni, Foteini Christidi, Georgia Karakitsiou, Vasiliki Georgousopoulou, Georgios Manomenidis, Dimitrios Tsiptsios, Konstantinos Vadikolias, Nikolaos Aggelousis and Pinelopi Vlotinou
Sensors 2025, 25(24), 7669; https://doi.org/10.3390/s25247669 - 18 Dec 2025
Cited by 2 | Viewed by 2822
Abstract
The progressive nature of dementia necessitates early detection strategies capable of identifying preclinical cognitive decline. Gait disturbances, mediated by higher-order cognitive functions, have emerged as potential digital biomarkers in this context. This bibliometric review systematically maps the scientific output from 2010 to 2025 [...] Read more.
The progressive nature of dementia necessitates early detection strategies capable of identifying preclinical cognitive decline. Gait disturbances, mediated by higher-order cognitive functions, have emerged as potential digital biomarkers in this context. This bibliometric review systematically maps the scientific output from 2010 to 2025 on the application of wearable sensor technologies and gait analysis in the early diagnosis of dementia. A targeted search of the Scopus database yielded 126 peer-reviewed studies, which were analyzed using VOSviewer for performance metrics, co-authorship networks, bibliographic coupling, co-citation, and keyword co-occurrence. The findings delineate a multidisciplinary research landscape, with major contributions spanning neurology, geriatrics, biomedical engineering, and computational sciences. Four principal thematic clusters were identified: (1) Cognitive and Clinical Aspects of Dementia, (2) Physical Activity and Mobility in Older Adults, (3) Technological and Analytical Approaches to Gait and Frailty and (4) Aging, Cognitive Decline, and Emerging Technologies. Despite the proliferation of research, significant gaps persist in longitudinal validation, methodological standardization, and integration into clinical workflows. This review emphasizes the potential of sensor-derived gait metrics to augment early diagnostic protocols and advocates for interdisciplinary collaboration to advance scalable, non-invasive diagnostic solutions for neurodegenerative diseases. Full article
(This article belongs to the Special Issue Advancing Human Gait Monitoring with Wearable Sensors)
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