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Sensors and Emerging Measurement Technologies in Lower Limb Biomechanics and Rehabilitation

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

Deadline for manuscript submissions: 20 February 2026 | Viewed by 5154

Special Issue Editors

Prof. Dr. Roozbeh Naemi

E-Mail Website
Guest Editor
Centre for Human Movement and Rehabilitation, School of Health and Society, University of Salford, Fredrick Road, Manchester M6 6PU, UK
Interests: lantar soft tissue; ultrasound elastography; soft tissue biomechanics; diabetic foot Ulcer; thermal imaging; skin microcirculation; orthotic insole; prognostic models
Prof. Dr. Liudi Jiang

E-Mail Website
Guest Editor
Mechanical Engineering Department, Faculty of Engineering and Physical Sciences, School of Engineering, University of Southampton, Highfield Campus, Southampton SO17 1BJ, UK
Interests: biomedical sensors; healthcare; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Lower limb function is essential for maintaining mobility.  Emerging technologies such as wearable sensors, imaging, and motion capture offer transformative approaches to healthcare by allowing advanced measurements to be taken not only in the lab but in real-world environments.

This Special Issue explores novel approaches and solutions that support the use of emerging measurement technologies for lower limb biomechanics and rehabilitation. Innovative tools, such as smart insoles or pedobarographic systems, along with associated signal processing, offer the potential to conduct investigations of the interface load distribution and temperature, particularly for prosthetic or orthotic users. Inertial Measurement Units, together with analytical methods, are also used to study lower limb kinematics and gait biomechanics. Additionally, motion capture technologies combined with machine learning and artificial intelligence are becoming more widely used to assess gait dysfunction, enabling the early detection of underlying health issues and evaluation of rehabilitation effectiveness across diverse settings.

We welcome contributions of original research papers and comprehensive reviews on this topic (subject to editorial pre-approval).

Prof. Dr. Roozbeh Naemi
Prof. Dr. Liudi Jiang
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 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

  • rehabilitation
  • prosthetics
  • orthotics
  • wearable sensors
  • signal processing
  • motion capture
  • image processing
  • lower limb biomechanics
  • foot biomechanics

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Published Papers (2 papers)

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Research

14 pages, 1611 KB  
Article
Predicting Running Vertical Ground Reaction Forces Using Neural Network Models Based on an IMU Sensor
by Shangxiao Li, Jiahui Pan, Dongmei Wang, Shufang Yuan, Jin Yang and Weiya Hao
Sensors 2025, 25(13), 3870; https://doi.org/10.3390/s25133870 - 21 Jun 2025
Cited by 1 | Viewed by 2474
Abstract
Vertical ground reaction force (vGRF) plays an important role in the study of running-related injuries (RRIs). This study explores the synchronization method between inertial measurement unit (IMU) and vGRF data of running and develops ANN models to accurately predict vGRF. Fifteen runners participated [...] Read more.
Vertical ground reaction force (vGRF) plays an important role in the study of running-related injuries (RRIs). This study explores the synchronization method between inertial measurement unit (IMU) and vGRF data of running and develops ANN models to accurately predict vGRF. Fifteen runners participated in this study. Acceleration data and vGRF values of eight rearfoot strikers and seven forefoot strikers running at 12, 14, and 16 km/h were collected by a single IMU and an instrumented treadmill. The sliding time window synchronization (STWS) algorithm was developed to sync IMU data with vGRF data. The wavelet neural network model (WNN) and feed-forward neural network model (FFNN) were adapted to predict vGRF using three-axis or sagittal-axis acceleration data in the stance phase, respectively. One rearfoot striker and one forefoot striker were randomly selected as a test set, while the other participants formed training sets. After synchronization, mean absolute errors for stride time of the IMU and vGRF data were less than 11.2 ms. The coefficient of multiple correlations for vGRF measured curves and predicted curves was more than 0.97. The normalized root mean square errors (NRMSEs) between two curves were 4.6~9.2%, and R2 was 0.93~0.99. For peak vGRF, the NRMSEs were 1.6~8.2%, except for rearfoot strike runners at 16 km/h using the FFNN model (10.7% and 11.1%). The Bland–Altman plots indicate that the errors for both the WNN and FFNN models are within acceptable limits. The STWS algorithm can effectively achieve the data synchronization between the IMU and the force plate during running. Both WNN and FFNN models demonstrated good accuracy and agreement in predicting vGRF. Using sagittal-axis acceleration data may be an ideal model with good prediction accuracy and less input data. This work provides direction for developing ANN models of personalized monitoring of lower limb load. Full article
(This article belongs to the Special Issue Sensors and Emerging Measurement Technologies in Lower Limb Biomechanics and Rehabilitation)
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25 pages, 5546 KB  
Article
A Portable Insole System for Actively Controlled Offloading of Plantar Pressure for Diabetic Foot Care
by Pedro Castro-Martins, Arcelina Marques, Luís Pinto-Coelho, Pedro Fonseca and Mário Vaz
Sensors 2025, 25(12), 3820; https://doi.org/10.3390/s25123820 - 19 Jun 2025
Cited by 2 | Viewed by 2302
Abstract
Plantar pressure monitoring is decisive in injury prevention, especially in at-risk populations such as people with diabetic foot. In this context, innovative solutions such as pneumatic insoles can be essential in plantar pressure management. This study describes the development of a variable pressure [...] Read more.
Plantar pressure monitoring is decisive in injury prevention, especially in at-risk populations such as people with diabetic foot. In this context, innovative solutions such as pneumatic insoles can be essential in plantar pressure management. This study describes the development of a variable pressure system that promotes the monitoring, stabilization, and offloading of plantar pressure through a pneumatic insole. This research was also intended to evaluate its ability to redistribute plantar pressure, reduce peak pressure in both static and dynamic conditions, and validate its pressure measurements by comparing the results with those obtained from a pedar® insole. Tests were carried out under both static and dynamic conditions, before and after the pressure stabilization process by air cells and the subsequent pressure offloading. During the validation process, methods were used to evaluate the agreement between measurements obtained by the two systems. The results of the static test showed that pressure stabilization reduced pressure on the heel by 32.43%, distributing it to the metatarsals and toes. After heel pressure offloading, the reduction reached 42.72%. In the dynamic test, despite natural dispersion of the measurements, a trend to reduce the peak pressure in the heel, metatarsals, and toes was observed. Agreement analysis recorded 96.32% in the static test and 94.02% in the dynamic test. The pneumatic insole proved effective in redistributing and reducing plantar pressure, with more evident effects in the static test. Its agreement with the pedar® system reinforces its reliability as a tool for measuring and managing plantar pressure, representing a promising solution for preventing plantar lesions. Full article
(This article belongs to the Special Issue Sensors and Emerging Measurement Technologies in Lower Limb Biomechanics and Rehabilitation)
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