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Advanced Sensors in Biomechanics and Rehabilitation Applications
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Advanced Sensors in Biomechanics and Rehabilitation Applications

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 4383

Special Issue Editor

Prof. Dr. Siamak Noroozi

E-Mail Website
Guest Editor
School of Design, Engineering and Computing, Bournemouth University, Talbot Campus, Fern Barrow, Poole, Dorset BH12 5BB, UK
Interests: biomechanics; human motion analysis

Special Issue Information

Dear Colleagues,

This Special Issue will cover a wide range of topics, from wearable sensors and smart textiles to implantable devices and advanced imaging techniques. The research within will highlight the role of these sensors in capturing and quantifying biomechanical parameters such as joint angles, muscle activity, gait patterns, and forces exerted during movement. These measurements seek to provide valuable insights into the mechanics of the human body and help clinicians to make informed decisions regarding diagnosis, treatment, and rehabilitation planning.

Furthermore, this Issue will present cutting-edge research on sensor-based technologies for rehabilitation interventions, showcasing how sensors are employed in the development of virtual reality systems, exoskeletons, and robotic prostheses to enhance motor learning, improve functional outcomes, and promote patient engagement. Additionally, this publication will explore the integration of sensor data with machine learning algorithms and artificial intelligence techniques, enabling personalized and adaptive rehabilitation approaches tailored to individual patient needs.

Overall, "Advanced Sensors in Biomechanics and Rehabilitation Applications" presents a comprehensive collection of research articles that showcase the transformative impact of sensor technologies in the fields of biomechanics and rehabilitation. We aim to provide valuable insights into the latest advancements, challenges, and future directions of sensor-based healthcare solutions, ultimately paving the way for improved diagnostics, optimized treatment strategies, and enhanced quality of life for patients.

Prof. Dr. Siamak Noroozi
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

  • biomechanics of shoulder and deltoid tension assessment
  • biomechanics of knee after total TKR
  • novel control valve design
  • novel control theories
  • smart prosthetic socket
  • prosthetics and spine kinematics
  • prosthetic eye technologies
  • composite foot dynamics (elastic response to impulse)
  • condition monitoring of rotating machinaries
  • study of joint dynamics and human motion analysis
  • composite ESR foot vibration and modal analysis

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

Published Papers (2 papers)

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15 pages, 2738 KiB  
Article
Cervical Sensorimotor Function Tests Using a VR Headset—An Evaluation of Concurrent Validity
by Karin Forsberg, Johan Jirlén, Inger Jacobson and Ulrik Röijezon
Sensors 2024, 24(17), 5811; https://doi.org/10.3390/s24175811 - 7 Sep 2024
Viewed by 2725
Abstract
Sensorimotor disturbances such as disturbed cervical joint position sense (JPS) and reduced reaction time and velocity in fast cervical movements have been demonstrated in people with neck pain. While these sensorimotor functions have been assessed mainly in movement science laboratories, new sensor technology [...] Read more.
Sensorimotor disturbances such as disturbed cervical joint position sense (JPS) and reduced reaction time and velocity in fast cervical movements have been demonstrated in people with neck pain. While these sensorimotor functions have been assessed mainly in movement science laboratories, new sensor technology enables objective assessments in the clinic. The aim was to investigate concurrent validity of a VR-based JPS test and a new cervical reaction acuity (CRA) test. Twenty participants, thirteen asymptomatic and seven with neck pain, participated in this cross-sectional study. The JPS test, including outcome measures of absolute error (AE), constant error (CE), and variable error (VE), and the CRA test, including outcome measures of reaction time and maximum velocity, were performed using a VR headset and compared to a gold standard optical motion capture system. The mean bias (assessed with the Bland–Altman method) between VR and the gold standard system ranged from 0.0° to 2.4° for the JPS test variables. For the CRA test, reaction times demonstrated a mean bias of −19.9 milliseconds (ms), and maximum velocity a mean bias of −6.5 degrees per seconds (°/s). The intraclass correlation coefficients (ICCs) between VR and gold standard were good to excellent (ICC 0.835–0.998) for the JPS test, and excellent (ICC 0.931–0.954) for reaction time and maximum velocity for the CRA test. The results show acceptable concurrent validity for the VR technology for assessment of JPS and CRA. A slightly larger bias was observed in JPS left rotation which should be considered in future research. Full article
(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation Applications)
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16 pages, 9706 KiB  
Article
Using Flexible-Printed Piezoelectric Sensor Arrays to Measure Plantar Pressure during Walking for Sarcopenia Screening
by Shulang Han, Qing Xiao, Ying Liang, Yu Chen, Fei Yan, Hui Chen, Jirong Yue, Xiaobao Tian and Yan Xiong
Sensors 2024, 24(16), 5189; https://doi.org/10.3390/s24165189 - 11 Aug 2024
Viewed by 1150
Abstract
Sarcopenia is an age-related syndrome characterized by the loss of skeletal muscle mass and function. Community screening, commonly used in early diagnosis, usually lacks features such as real-time monitoring, low cost, and convenience. This study introduces a promising approach to sarcopenia screening by [...] Read more.
Sarcopenia is an age-related syndrome characterized by the loss of skeletal muscle mass and function. Community screening, commonly used in early diagnosis, usually lacks features such as real-time monitoring, low cost, and convenience. This study introduces a promising approach to sarcopenia screening by dynamic plantar pressure monitoring. We propose a wearable flexible-printed piezoelectric sensing array incorporating barium titanate thin films. Utilizing a flexible printer, we fabricate the array with enhanced compressive strength and measurement range. Signal conversion circuits convert charge signals of the sensors into voltage signals, which are transmitted to a mobile phone via Bluetooth after processing. Through cyclic loading, we obtain the average voltage sensitivity (4.844 mV/kPa) of the sensing array. During a 6 m walk, the dynamic plantar pressure features of 51 recruited participants are extracted, including peak pressures for both sarcopenic and control participants before and after weight calibration. Statistical analysis discerns feature significance between groups, and five machine learning models are employed to screen for sarcopenia with the collected features. The results show that the features of dynamic plantar pressure have great potential in early screening of sarcopenia, and the Support Vector Machine model after feature selection achieves a high accuracy of 93.65%. By combining wearable sensors with machine learning techniques, this study aims to provide more convenient and effective sarcopenia screening methods for the elderly. Full article
(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation Applications)
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