Intelligent Evaluation Method of Human Cervical Vertebra Rehabilitation Based on Computer Vision

Du, Qiwei; Bai, H Manjula; Zhu, Zhongpan

doi:10.3390/s23083825

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…Recently, many digital alternatives have emerged as accessible methods for human pose estimation, such as MediaPipe [15], OpenPose [16], MoveNet [17], and PoseNet [18]. Their strength lies in the possibility of performing objective and reproducible analyses with a simple video or photo achieved in any environment [19,20]. Such methods are being employed in different scenarios.…”

Section: Introductionmentioning

confidence: 99%

Biomechanical Posture Analysis in Healthy Adults with Machine Learning: Applicability and Reliability

Roggio,

Di Grande,

Cavalieri

et al. 2024

Sensors

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Posture analysis is important in musculoskeletal disorder prevention but relies on subjective assessment. This study investigates the applicability and reliability of a machine learning (ML) pose estimation model for the human posture assessment, while also exploring the underlying structure of the data through principal component and cluster analyses. A cohort of 200 healthy individuals with a mean age of 24.4 ± 4.2 years was photographed from the frontal, dorsal, and lateral views. We used Student’s t-test and Cohen’s effect size (d) to identify gender-specific postural differences and used the Intraclass Correlation Coefficient (ICC) to assess the reliability of this method. Our findings demonstrate distinct sex differences in shoulder adduction angle (men: 16.1° ± 1.9°, women: 14.1° ± 1.5°, d = 1.14) and hip adduction angle (men: 9.9° ± 2.2°, women: 6.7° ± 1.5°, d = 1.67), with no significant differences in horizontal inclinations. ICC analysis, with the highest value of 0.95, confirms the reliability of the approach. Principal component and clustering analyses revealed potential new patterns in postural analysis such as significant differences in shoulder–hip distance, highlighting the potential of unsupervised ML for objective posture analysis, offering a promising non-invasive method for rapid, reliable screening in physical therapy, ergonomics, and sports.

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Section: Introductionmentioning

confidence: 99%

Biomechanical Posture Analysis in Healthy Adults with Machine Learning: Applicability and Reliability

Roggio,

Di Grande,

Cavalieri

et al. 2024

Sensors

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“…Mou, Fangli et al 20 proposed a practical approach for the complex task of robotic cable insertion. Du et al 21 introduced a multi-task computer vision-based approach for assessing neck exercise, which can server as a substitute for physician-guided rehabilitation and evaluation, particularly beneficial for adolescents.…”

Section: Introductionmentioning

confidence: 99%

Combined MediaPipe and YOLOv5 range of motion assessment system for spinal diseases and frozen shoulder

Zhang,

Li,

Cai

et al. 2024

Sci Rep

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Spinal diseases and frozen shoulder are prevalent health problems in Asian populations. Early assessment and treatment are very important to prevent the disease from getting worse and reduce pain. In the field of computer vision, it is a challenging problem to assess the range of motion. In order to realize efficient, real-time and accurate assessment of the range of motion, an assessment system combining MediaPipe and YOLOv5 technologies was proposed in this study. On this basis, Convolutional Block Attention Module (CBAM) is introduced into the YOLOv5 target detection model, which can enhance the extraction of feature information, suppress background interference, and improve the generalization ability of the model. In order to meet the requirements of large-scale computing, a client/server (C/S) framework structure is adopted. The evaluation results can be obtained quickly after the client uploads the image data, providing a convenient and practical solution. In addition, a game of "Picking Bayberries" was developed as an auxiliary treatment method to provide patients with interesting rehabilitation training.

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A novel multiagent system for cervical motor control evaluation and individualized therapy: integrating gamification and portable solutions

Sales Mendes,

Sánchez San Blas,

Pérez Robledo

et al. 2024

Multimedia Systems

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The study focused on designing a portable, objective device for assessing and addressing Cervical Motor Control (CMC) impairments. This device is based on a proposed architecture that employs advanced technology to evaluate and enhance patients’ CMC. During a pilot study with 10 participants, the device’s feasibility and usability were verified, including an initial assessment using the Head Relocation Test and a 12-session intervention over 4 weeks. The architecture of the proposed system is responsible for gathering pertinent data concerning patients’ cervical motor control. It employs advanced algorithms to process this data and objectively assess CMC function. Furthermore, the system tailors the therapy to each patient’s individual needs. Preliminary results indicate that the device and the proposed architecture positively impact assessment test performance accuracy. While additional validation tests are required to confirm their effectiveness, this device emerges as a promising and valuable alternative for assessing and treating patients with CMC impairments. Its focus on advanced technology and personalized adaptation aligns with previous research in telerehabilitation and classroom physical activity promotion.

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Intelligent Evaluation Method of Human Cervical Vertebra Rehabilitation Based on Computer Vision

Cited by 3 publications

References 37 publications

Biomechanical Posture Analysis in Healthy Adults with Machine Learning: Applicability and Reliability

Biomechanical Posture Analysis in Healthy Adults with Machine Learning: Applicability and Reliability

Combined MediaPipe and YOLOv5 range of motion assessment system for spinal diseases and frozen shoulder

A novel multiagent system for cervical motor control evaluation and individualized therapy: integrating gamification and portable solutions

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