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Applied Sciences
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Advanced Sensors for Postural or Gait Stability Assessment
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Advanced Sensors for Postural or Gait Stability Assessment

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 November 2024 | Viewed by 2982

Special Issue Editor

Prof. Dr. Lingfeng Shi

E-Mail Website
Guest Editor
College of Electronic Engineering, Xidian University, Xi'an, China
Interests: sensor signal processing; positioning and navigation; wireless communication

Special Issue Information

Dear Colleagues,

As society has developed, there is an increasing demand for human pose recognition and detection. The development of wearable sensors and artificial intelligence technology provides has helped to solve this problem. It can be widely applied in various fields such as medical treatment, rehabilitation, intelligent care, and autonomous positioning. This Special Issue aims to publish relevant research academic papers to solve such problems, providing support for promoting the development of human society. Therefore, we sincerely invite scholars to actively submit papers on advanced sensor signal processing. These papers are expected to utilize artificial intelligence technology, but are not limited to this field. Relevant sensors for attitude detection or recognition are expected to be published.

Prof. Dr. Lingfeng Shi
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. Applied Sciences 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 2400 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

  • gait recognition
  • wearable sensors
  • MEMS
  • posture detection
  • deep learning
  • CNN
  • bidirectional LSTM

Published Papers (4 papers)

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Research

19 pages, 3122 KiB  
Article
Enhancing Human Key Point Identification: A Comparative Study of the High-Resolution VICON Dataset and COCO Dataset Using BPNET
by Yunju Lee, Bibash Lama, Sunghwan Joo and Jaerock Kwon
Appl. Sci. 2024, 14(11), 4351; https://doi.org/10.3390/app14114351 - 21 May 2024
Viewed by 284
Abstract
Accurately identifying human key points is crucial for various applications, including activity recognition, pose estimation, and gait analysis. This study introduces a high-resolution dataset formed via the VICON motion capture system and three diverse 2D cameras. It facilitates the training of neural networks [...] Read more.
Accurately identifying human key points is crucial for various applications, including activity recognition, pose estimation, and gait analysis. This study introduces a high-resolution dataset formed via the VICON motion capture system and three diverse 2D cameras. It facilitates the training of neural networks to estimate 2D key joint positions from images and videos. The study involved 25 healthy adults (17 males, 8 females), executing normal gait for 2 to 3 s. The VICON system captured 3D ground truth data, while the three 2D cameras collected images from different perspectives (0°, 45°, and 135°). The dataset was used to train the Body Pose Network (BPNET), a popular neural network model developed by NVIDIA TAO. Additionally, a comparison entails another BPNET model trained on the COCO 2017 dataset, featuring over 118,000 annotated images. Notably, the proposed dataset exhibited a higher level of accuracy (14.5%) than COCO 2017, despite comprising one-fourth of the image count (23,741 annotated image). This substantial reduction in data size translates to improvements in computational efficiency during model training. Furthermore, the unique dataset’s emphasis on gait and precise prediction of key joint positions during normal gait movements distinguish it from existing alternatives. This study has implications ranging from gait-based person identification, and non-invasive concussion detection through sports temporal analysis, to pathologic gait pattern identification. Full article
(This article belongs to the Special Issue Advanced Sensors for Postural or Gait Stability Assessment)
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17 pages, 3928 KiB  
Article
Computer Vision System Based on the Analysis of Gait Features for Fall Risk Assessment in Elderly People
by Rogelio Cedeno-Moreno, Diana L. Malagon-Barillas, Luis A. Morales-Hernandez, Mayra P. Gonzalez-Hernandez and Irving A. Cruz-Albarran
Appl. Sci. 2024, 14(9), 3867; https://doi.org/10.3390/app14093867 - 30 Apr 2024
Viewed by 463
Abstract
Up to 30% of people over the age of 60 are at high risk of falling, which can cause injury, aggravation of pre-existing conditions, or even death, with up to 684,000 fatal falls reported annually. This is due to the difficult task of [...] Read more.
Up to 30% of people over the age of 60 are at high risk of falling, which can cause injury, aggravation of pre-existing conditions, or even death, with up to 684,000 fatal falls reported annually. This is due to the difficult task of establishing a preventive system for the care of the elderly, both in the hospital environment and at home. Therefore, this work proposes the development of an intelligent vision system that uses a novel methodology to infer fall risk from the analysis of kinetic and spatiotemporal gait parameters. In general, each patient is assessed using the Tinetti scale. Then, the computer vision system estimates the biomechanics of walking and obtains gait features, such as stride length, cadence, period, and range of motion. Subsequently, this information serves as input to an artificial neural network that diagnoses the risk of falling. Ninety-six participants took part in the study. The system’s performance was 99.1% accuracy, 94.4% precision, 96.9% recall, 99.4% specificity, and 95.5% F1-Score. Thus, the proposed system can evaluate the fall risk assessment, which could benefit clinics, hospitals, and even homes by allowing them to assess in real time whether a person is at high risk of falling to provide timely assistance. Full article
(This article belongs to the Special Issue Advanced Sensors for Postural or Gait Stability Assessment)
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23 pages, 8148 KiB  
Article
A Quick Capture Evaluation System for the Automatic Assessment of Work-Related Musculoskeletal Disorders for Sanitation Workers
by Ruiqiu Zhang and Minxin Huang
Appl. Sci. 2024, 14(4), 1542; https://doi.org/10.3390/app14041542 - 14 Feb 2024
Cited by 1 | Viewed by 776
Abstract
Musculoskeletal disorders not only impact workers’ health but also result in significant economic losses to society. Sanitation workers often have to lift waste bags from containers, leading to shoulder joint flexion of 90° or more, exposing them to hazardous environments for extended periods. [...] Read more.
Musculoskeletal disorders not only impact workers’ health but also result in significant economic losses to society. Sanitation workers often have to lift waste bags from containers, leading to shoulder joint flexion of 90° or more, exposing them to hazardous environments for extended periods. This study combines deep learning and image recognition to create a Quick Capture Evaluation System (QCES). By comparing body angles captured in the sanitation workers’ work environment with those from OptiTrack motion capture, the system showed an average Root Mean Square Error of 5.64 for 18 different postures, and an average Spearman’s rho of 0.87, indicating its precision. Compared with scores assessed by three experts, the system demonstrated an average Cohen’s kappa of 0.766, proving its reliability. Practical assessments of sanitation workers revealed that tilting the waste containers could significantly improve their posture and reduce the risk of Work-Related Musculoskeletal Disorders. It proves that the QCES system can accurately and rapidly assess the on-site posture of a particular occupation. Full article
(This article belongs to the Special Issue Advanced Sensors for Postural or Gait Stability Assessment)
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18 pages, 9101 KiB  
Article
Design of Liquid–Air Hybrid Cooling Garment and Its Effect on Local Thermal Comfort
by Wanwan Wang and Mengmeng Zhao
Appl. Sci. 2023, 13(16), 9414; https://doi.org/10.3390/app13169414 - 19 Aug 2023
Cited by 1 | Viewed by 912
Abstract
Personal cooling garments were reported effective in improving thermal comfort in hot environments. In this study, three liquid–air hybrid cooling garments and one control garment were designed and made: aluminum-tube fan cooling (AAL), silicone-tube fan cooling (SAL), silicone-tube fan cooling with inner yarn [...] Read more.
Personal cooling garments were reported effective in improving thermal comfort in hot environments. In this study, three liquid–air hybrid cooling garments and one control garment were designed and made: aluminum-tube fan cooling (AAL), silicone-tube fan cooling (SAL), silicone-tube fan cooling with inner yarn fabric (YAL), and a control garment (CON) without the cooling sources. Subject trials were performed by eight female subjects in a climate chamber to simulate a summer indoor working environment at 32 °C and 50% relative humidity. The results showed that the liquid–air hybrid cooling garment provided effective convective and conductive heat dissipation compared with the no-cooling (CON) stat, chest, belly, shoulder, back, hand, thigh, and calf. The horizontal e, resulting in a decrease in local body skin temperature. Compared with the CON, the liquid–air cooling garment resulted in a maximum reduction of 1 °C for the mean torso skin temperature and 1.5 °C for the localized shoulder skin temperature. The AAL had a better cooling effect on the torso skin temperature compared with the SAL, and the cooling of the AAL was 0.5 °C lower than that of the SAL for the shoulder skin temperature. The presented liquid–air hybrid cooling garments were effective in cooling the body and improving thermal comfort. They were portable, accessible, and sustainable in hot indoor environments compared with air conditioners. Therefore, they could save energy. Full article
(This article belongs to the Special Issue Advanced Sensors for Postural or Gait Stability Assessment)
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