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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 7089

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Special Issue Editor

Prof. Dr. Lingfeng Shi

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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

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Keywords

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

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Published Papers (7 papers)

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Research

12 pages, 4578 KiB

Open AccessArticle

Gait Analysis of Elderly and Young People Based on Joint Angles and Positions

by Mizuki Saga and Norihiko Saga

Appl. Sci. 2024, 14(21), 9646; https://doi.org/10.3390/app14219646 - 22 Oct 2024

Viewed by 438

Abstract

For the elderly, the motor function of walking is significant for the maintenance and improvement of their quality of life (QOL). Walking patterns have been postulated to be associated with a decline in walking ability. The objective of this study was to develop a wearable sensor that can capture changes in walking ability in daily life. To this end, motion capture was employed to measure the lower limb joint angles and the trajectories of the lower limbs and knees for each subject while walking at a normal walking speed and to compare the results between elderly and young patients. The differences in walking patterns between the elderly and young patients were analyzed in terms of elevation angles, and an index was calculated to capture changes during walking based on elevation angles and trajectories. Full article

(This article belongs to the Special Issue Advanced Sensors for Postural or Gait Stability Assessment)

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17 pages, 2175 KiB

Open AccessArticle

Real-Time Forward Head Posture Detection and Correction System Utilizing an Inertial Measurement Unit Sensor

by Gyumin Park and Im Y. Jung

Appl. Sci. 2024, 14(19), 9075; https://doi.org/10.3390/app14199075 - 8 Oct 2024

Viewed by 625

Abstract

Forward head posture (FHP) has become a prevailing health issue in modern society as people spend more time on computers and smartphones. FHP is a posture where the head is forward and the anterior and posterior curvatures of the lower cervical and upper thoracic spines are both, respectively, exaggerated. FHP is often associated with neck pain, bad static balance, and hunched shoulders or back. To prevent this, consciously maintaining good posture is important. Therefore, in this study, we propose a system that gives users real-time, accurate information about their neck posture, and it also encourages them to maintain a good posture. This inexpensive system utilizes a single inertial measurement unit sensor and a Raspberry Pi system to detect the changes in state that can progress to an FHP. It retrieves data from the sensor attached to the user’s cervical spine to indicate their real-time posture. In a real-world office environment experiment with ten male participants, the system accurately detected the transition to the FHP state for more than 10 s, with a delay of less than 0.5 s, and it also provided personalized feedback to encourage them to maintain good posture. All ten participants recognized that their average craniovertebral angle had to be increased after receiving visual alerts regarding their poor postures in real time. The results indicate that the system has potential for widespread applications. Full article

(This article belongs to the Special Issue Advanced Sensors for Postural or Gait Stability Assessment)

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26 pages, 29327 KiB

Open AccessArticle

Supporter-Type Anterior Cruciate Ligament Prevention System Based on Estimation of Knee Joint Valgus Angle Using Stretch Sensors

by Ayumi Ohnishi, Ryosuke Takegawa, Kazuhiko Hirata, Minoru Toriyama, Tsutomu Terada and Masahiko Tsukamoto

Appl. Sci. 2024, 14(14), 6210; https://doi.org/10.3390/app14146210 - 17 Jul 2024

Viewed by 760

Abstract

Anterior cruciate ligament (ACL) injuries are common in sports involving jumping and rapid direction changes, often occurring in non-contact situations. The risk of ACL injury is evaluated by knee flexion and valgus angles; a small knee flexion angle combined with a large valgus angle increases the risk. Monitoring these angles during activities can help athletes recognize their ACL injury risk and adjust their movements. Traditional 3D motion analysis, used for measuring knee angles, is costly and impractical for daily practice. This study proposes a knee supporter with stretch sensors to estimate knee flexion and valgus angles in practice settings, evaluating ACL injury risk and notifying athletes of high-risk movements. The proposed device wirelessly transmits data from three stretch sensors placed on the device to a PC and uses machine learning to estimate the knee angles. The results of the evaluation experiments, conducted with data from five healthy male and female participants in their twenties, indicate that the estimation accuracy for the knee flexion angle, achieved by a model trained using a Random Forest Regressor (RFR) with data from individuals other than the target user, resulted in a Mean Absolute Error (MAE) of 8.86 degrees. For the knee valgus angle, a model trained with the user’s own data using the RFR achieved a MAE of 0.81 degrees. Full article

(This article belongs to the Special Issue Advanced Sensors for Postural or Gait Stability Assessment)

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19 pages, 3122 KiB

Open AccessArticle

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 812

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 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

Open AccessArticle

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 1154

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 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

Open AccessArticle

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 1168

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. 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

Open AccessArticle

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 4 | Viewed by 1342

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 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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