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Bioengineering
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Advanced Wearable Sensors for Human Gait Analysis
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Advanced Wearable Sensors for Human Gait Analysis

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 2612

Special Issue Editors

Dr. Tong Li

E-Mail Website
Guest Editor
College of Education, Zhejiang University, Hangzhou, China
Interests: gait biomechanics; musculoskeletal modeling and simulation; wearable sensors; assistive robotics
Prof. Dr. Qingguo Li

E-Mail Website
Guest Editor
Department of Mechanical and Materials Engineering, Queen’s University, Kingston, ON K7L 3N6, Canada
Interests: biomechanical system design; energy harvesting; wearable sensors; gait analysis; load carriage
Dr. Bingfei Fan

E-Mail Website
Guest Editor
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Interests: wearable sensors; human motion analysis; rehabilitation robot
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of wearable sensors has revolutionized how we monitor and analyze human movement, particularly in the field of gait analysis. Understanding and assessing human gait is essential for a variety of applications, including rehabilitation, clinical diagnostics, sport performance, and injury prevention. The integration of wearable sensor technologies offers the potential for real-time, personalized, and unobtrusive monitoring of gait, providing valuable insights into both healthy and pathological movement patterns.

This Special Issue seeks to highlight the latest research, innovations, and applications in the field of wearable sensors for human gait analysis. We invite original research articles, reviews, and case studies that explore the development, application, and impact of wearable technologies for capturing and analyzing human gait.

We are particularly interested in contributions that address the following areas:

  • Advancements in wearable sensor technologies;
  • Data processing and machine learning approaches;
  • Clinical applications and rehabilitation;
  • Wearable sensors in sports and performance optimization;
  • Real-time feedback systems for users during training and recovery;
  • User-centric design and implementation challenges.

Dr. Tong Li
Prof. Dr. Qingguo Li
Dr. Bingfei Fan
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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 movement analysis
  • rehabilitation
  • sport science
  • sensor fusion
  • machine learning

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Published Papers (3 papers)

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Research

15 pages, 2160 KB  
Article
Evaluation of Parkinson’s Disease Motor Symptoms via Wearable Inertial Measurements Units and Surface Electromyography Sensors
by Xiangliang Zhang, Wenhao Pan, Zhuoneng Wu, Xiangzhi Liu, Yiping Sun, Bingfei Fan, Miao Cai, Tong Li and Tao Liu
Bioengineering 2025, 12(10), 1116; https://doi.org/10.3390/bioengineering12101116 - 18 Oct 2025
Viewed by 324
Abstract
Parkinson’s disease (PD) is one of the fastest-growing neurodegenerative disorders; its cardinal motor signs—tremor, bradykinesia, and rigidity—substantially impair quality of life. Conventional clinician-rated scales can be subjective and exhibit limited interrater reliability, underscoring the need for objective and reliable quantification. We present an [...] Read more.
Parkinson’s disease (PD) is one of the fastest-growing neurodegenerative disorders; its cardinal motor signs—tremor, bradykinesia, and rigidity—substantially impair quality of life. Conventional clinician-rated scales can be subjective and exhibit limited interrater reliability, underscoring the need for objective and reliable quantification. We present an integrated evaluation framework that leverages surface electromyography (sEMG) with multimodal sensing. For representation learning, we combine time–frequency descriptors with Mini-ROCKET features. Grading is performed by an sEMG-based Unified Parkinson’s Disease Rating Scale (UPDRS) model (LDA-SV) that produces per-segment probabilities for ordinal scores (0–3) and aggregates them via soft voting to assign item-level ratings. Participants completed a standardized protocol spanning gait, seated rest, and upper-limb tasks (forearm pronation–supination, finger-to-nose, fist clench, and thumb–index pinch). Using the aforementioned dataset, we report task-wise performance with 95% confidence intervals and compare the proposed model against CNN, LSTM, and InceptionTime using McNemar tests and log-odds ratios. The results indicate that the proposed model outperforms the three baseline models overall. These findings demonstrate the effectiveness and feasibility of the proposed approach, suggesting a viable pathway for the objective quantification of PD motor symptoms and facilitating broader clinical adoption of sEMG in diagnosis and treatment. Full article
(This article belongs to the Special Issue Advanced Wearable Sensors for Human Gait Analysis)
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27 pages, 6922 KB  
Article
Real-World Wrist-Derived Digital Mobility Outcomes in People with Multiple Long-Term Conditions: A Comparison of Algorithms
by Dimitrios Megaritis, Lisa Alcock, Kirsty Scott, Hugo Hiden, Andrea Cereatti, Ioannis Vogiatzis and Silvia Del Din
Bioengineering 2025, 12(10), 1108; https://doi.org/10.3390/bioengineering12101108 - 15 Oct 2025
Viewed by 512
Abstract
Digital Mobility outcomes can serve as objective biomarkers of health, but their validation in populations with multiple long-term conditions (MLTCs) based on wrist-worn devices remains unexplored. We refined, improved, and introduced novel algorithms, specifically tailored and adapted for (i) gait sequence detection, (ii) [...] Read more.
Digital Mobility outcomes can serve as objective biomarkers of health, but their validation in populations with multiple long-term conditions (MLTCs) based on wrist-worn devices remains unexplored. We refined, improved, and introduced novel algorithms, specifically tailored and adapted for (i) gait sequence detection, (ii) initial contact identification, and (iii) stride length estimation from a single wrist-worn device. Validation was performed using data from 28 participants with co-occurring MLTCs performing a 2.5 h real-world monitoring session. Reference data from an established multi-sensor system were used to assess algorithm performance across diverse gait patterns of co-occurring MLTCs. Twenty-eight participants (mean age 70.4 years, 43% females) had a median of three co-occurring MLTCs. Among six gait sequence detection methods, improved versions of the Kheirkhahan algorithm performed best (accuracy = 0.92, specificity = 0.96). For initial contact detection (nine methods tested), Shin’s algorithm achieved the highest performance index (0.85) followed by McCamley (0.84). Stride length estimation was most accurate using novel approaches based on the Weinberg method (performance index > 0.70). The proposed fine-tuned algorithms, the newly developed adaptive variants, and the foot-length augmented versions demonstrated robust performance, surpassing many existing methods and addressing the complexity of gait patterns in MLTCs. These findings enable scalable, real-time mobility monitoring in complex clinical populations using accessible wearable technology. Full article
(This article belongs to the Special Issue Advanced Wearable Sensors for Human Gait Analysis)
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17 pages, 5036 KB  
Article
Automated UPDRS Gait Scoring Using Wearable Sensor Fusion and Deep Learning
by Xiangzhi Liu, Xiangliang Zhang, Juan Li, Wenhao Pan, Yiping Sun, Shuanggen Lin and Tao Liu
Bioengineering 2025, 12(7), 686; https://doi.org/10.3390/bioengineering12070686 - 24 Jun 2025
Cited by 1 | Viewed by 1253
Abstract
The quantitative assessment of Parkinson’s disease (PD) is critical for guiding diagnosis, treatment, and rehabilitation. Conventional clinical evaluations—heavily dependent on manual rating scales such as the Unified Parkinson’s Disease Rating Scale (UPDRS)—are time-consuming and prone to inter-rater variability. In this study, we propose [...] Read more.
The quantitative assessment of Parkinson’s disease (PD) is critical for guiding diagnosis, treatment, and rehabilitation. Conventional clinical evaluations—heavily dependent on manual rating scales such as the Unified Parkinson’s Disease Rating Scale (UPDRS)—are time-consuming and prone to inter-rater variability. In this study, we propose a fully automated UPDRS gait-scoring framework. Our method combines (a) surface electromyography (EMG) signals and (b) inertial measurement unit (IMU) data into a single deep learning model. Our end-to-end network comprises three specialized branches—a diagnosis head, an evaluation head, and a balance head—whose outputs are integrated via a customized fusion-detection module to emulate the multidimensional assessments performed by clinicians. We validated our system on 21 PD patients and healthy controls performing a simple walking task while wearing a four-channel EMG array on the lower limbs and 2 shank-mounted IMUs. It achieved a mean classification accuracy of 92.8% across UPDRS levels 0–2. This approach requires minimal subject effort and sensor setup, significantly cutting clinician workload associated with traditional UPDRS evaluations while improving objectivity. The results demonstrate the potential of wearable sensor-driven deep learning methods to deliver rapid, reliable PD gait assessment in both clinical and home settings. Full article
(This article belongs to the Special Issue Advanced Wearable Sensors for Human Gait Analysis)
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