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Sensors for Biomechanical and Rehabilitation Engineering
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Sensors for Biomechanical and Rehabilitation Engineering

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 984

Special Issue Editor

Dr. Ziyun Ding

E-Mail Website
Guest Editor
School of Engineering, University of Birmingham, Edgbaston Campus, Birmingham B15 2TT, UK
Interests: gait analysis; musculoskeletal modelling and simulation; wearable IMU sensors; lower limb biomechanics

Special Issue Information

Dear Colleagues,

In the realm of biomechanics, wearable technology has emerged as a transformative tool in rehabilitation, utilising sensors to continuously monitor and provide real-time feedback on a wide range of data collected from individuals. This technology facilitates the assessment of biomechanical signals using force/pressure sensors, inertial measurement units (IMUs), and electromyography (EMG) sensors. By capturing these diverse data, wearable devices enable healthcare professionals to design personalised rehabilitation protocols, track patient progress, and optimise therapeutic interventions, ultimately enhancing recovery outcomes. 

This Special Issue therefore aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of Biomechanical and Rehabilitation Engineering.

Dr. Ziyun Ding
Guest Editor

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. 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

  • sensors
  • biomechanical
  • rehabilitation

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Published Papers (2 papers)

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Research

17 pages, 3439 KiB  
Article
A Novel Approach for Visual Speech Recognition Using the Partition-Time Masking and Swin Transformer 3D Convolutional Model
by Xiangliang Zhang, Yu Hu, Xiangzhi Liu, Yu Gu, Tong Li, Jibin Yin and Tao Liu
Sensors 2025, 25(8), 2366; https://doi.org/10.3390/s25082366 - 8 Apr 2025
Viewed by 251
Abstract
Visual speech recognition is a technology that relies on visual information, offering unique advantages in noisy environments or when communicating with individuals with speech impairments. However, this technology still faces challenges, such as limited generalization ability due to different speech habits, high recognition [...] Read more.
Visual speech recognition is a technology that relies on visual information, offering unique advantages in noisy environments or when communicating with individuals with speech impairments. However, this technology still faces challenges, such as limited generalization ability due to different speech habits, high recognition error rates caused by confusable phonemes, and difficulties adapting to complex lighting conditions and facial occlusions. This paper proposes a lip reading data augmentation method—Partition-Time Masking (PTM)—to address these challenges and improve lip reading models’ performance and generalization ability. Applying nonlinear transformations to the training data enhances the model’s generalization ability when handling diverse speakers and environmental conditions. A lip-reading recognition model architecture, Swin Transformer and 3D Convolution (ST3D), was designed to overcome the limitations of traditional lip-reading models that use ResNet-based front-end feature extraction networks. By adopting a strategy that combines Swin Transformer and 3D convolution, the proposed model enhances performance. To validate the effectiveness of the Partition-Time Masking data augmentation method, experiments were conducted on the LRW video dataset using the DC-TCN model, achieving a peak accuracy of 92.15%. The ST3D model was validated on the LRW and LRW1000 video datasets, achieving a maximum accuracy of 56.1% on the LRW1000 dataset and 91.8% on the LRW dataset, outperforming current mainstream lip reading models and demonstrating superior performance on challenging easily confused samples. Full article
(This article belongs to the Special Issue Sensors for Biomechanical and Rehabilitation Engineering)
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16 pages, 5668 KiB  
Article
Influence of Sampling Rate on Wearable IMU Orientation Estimation Accuracy for Human Movement Analysis
by Bingfei Fan, Luobin Zhang, Shibo Cai, Mingyu Du, Tao Liu, Qingguo Li and Peter Shull
Sensors 2025, 25(7), 1976; https://doi.org/10.3390/s25071976 - 22 Mar 2025
Viewed by 385
Abstract
Wearable inertial measurement units (IMUs) have been widely used in human movement analysis outside the laboratory. However, the IMU-based orientation estimation remains challenging, particularly in scenarios involving relatively fast movements. Increased sampling rate has the potential to improve accuracy, but it also increases [...] Read more.
Wearable inertial measurement units (IMUs) have been widely used in human movement analysis outside the laboratory. However, the IMU-based orientation estimation remains challenging, particularly in scenarios involving relatively fast movements. Increased sampling rate has the potential to improve accuracy, but it also increases power consumption and computational complexity. The relationship between sampling frequencies and accuracies remains unclear. We thus investigated the specific influence of IMU sampling frequency on orientation estimation across a spectrum of movement speeds and recommended sufficient sampling rates. Seventeen healthy subjects wore IMUs on their thigh, shank, and foot and performed walking (1.2 m/s) and running (2.2 m/s) trials on a treadmill, and a motion testbed with an IMU was used to mimic high-frequency cyclic human movements up to 3.0 Hz. Four widely used IMU sensor fusion algorithms computed orientations at 10, 25, 50, 100, 200, 400, 800, and 1600 Hz and were compared with marker-based optical motion capture (OMC) orientations to determine accuracy. Results suggest that the sufficient IMU sampling rate for walking is 100 Hz, running is 200 Hz, and high-speed cyclic movements is 400 Hz. The accelerometer sampling rate is less important than the gyroscope sampling rate. Further, accelerometer sampling rates exceeding 100 Hz even resulted in decreased accuracy because excessive orientation updates using distorted accelerations and angular velocity introduced more error than merely using angular velocity. These findings could serve as a foundation to inform wearable IMU development or selection across a spectrum of human gait movement speeds. Full article
(This article belongs to the Special Issue Sensors for Biomechanical and Rehabilitation Engineering)
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