A state-of-the-art integrative approach to studying neck biomechanics in vivo

Zhou, Yu; Chowdhury, S.; Reddy, Curran; Wan, Bocheng; Byrne, Ryan M.; Wei, Yin; Zhang, Xudong

doi:10.1007/s11431-020-1672-x

Cited by 5 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experimental data acquisition. In this experiment, participants performed selfpaced head-neck flexion-extension tasks (i.e., dynamic free range-of-motion tasks) with the head-neck moving primarily in the sagittal plane while their cervical spine region was imaged continuously using a DSX system (Zhou et al, 2020). A 12-camera Vicon motion capture system (Vantage-Series, Vicon Motion Labs, Oxford, UK) was used to monitor and record the head-neck motions from retro-reflective spherical surface markers placed on ten anatomical landmarks: glabellae, inferior border of each orbit (left and right), the tragion notches (left and right), acromion processes (left and right), suprasternal notch, sternum, and C7 spinous process as shown in Figure . 8.…”

Section: Calibration Points (X C Y C )mentioning

confidence: 99%

Renewing Iterative Self-labeling Domain Adaptation with Application to the Spine Motion Prediction

Chen¹,

Zhou²,

Zhang³

et al. 2022

Preprint

View full text Add to dashboard Cite

The area of transfer learning comprises supervised machine learning methods that cope with the issue when the training and testing data have different input feature spaces or distributions. In this work, we propose a novel transfer learning algorithm called Renewing Iterative Self-labeling Domain Adaptation (Re-ISDA). This work is motivated by a cervical spine motion prediction problem, with the goal of predicting the cervical spine motion of different subjects using the measurements of "exterior features". The joint distribution of the exterior features for each subject may vary with one's distinct BMI, sex and other characteristics; the sample size of this problem is limited due to the high experimental cost. The proposed method is well suited for transfer learning problems with limited training samples. The learning problem is formulated as a dynamic programming model and the latter is then solved by an efficient greedy algorithm. Numerical studies show that the proposed method outperforms prevailing transfer learning methods. The proposed method also achieves high prediction accuracy for the cervical spine motion problem.

show abstract

Section: Calibration Points (X C Y C )mentioning

confidence: 99%

Renewing Iterative Self-labeling Domain Adaptation with Application to the Spine Motion Prediction

Chen¹,

Zhou²,

Zhang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Inverse kinematics in cervical spine models: Effects of scaling and model degrees of freedom for extension and flexion movements

Barnamehei,

Zhou,

Zhang

et al. 2024

Journal of Biomechanics

View full text Add to dashboard Cite

The deflection of fatigued neck

Zhou,

Reddy,

Zhang

2024

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

The human neck is a unique mechanical structure, highly flexible but fatigue prone. The rising prevalence of neck pain and chronic injuries has been attributed to increasing exposure to fatigue loading in activities such as prolonged sedentary work and overuse of electronic devices. However, a causal relationship between fatigue and musculoskeletal mechanical changes remains elusive. This work aimed to establish this relationship through a unique experiment design, inspired by a cantilever beam mechanical model of the neck, and an orchestrated deployment of advanced motion-force measurement technologies including dynamic stereo-radiographic imaging. As a group of 24 subjects performed sustained-till-exhaustion neck exertions in varied positions—neutral, extended, and flexed, their cervical spine musculoskeletal responses were measured. Data verified the occurrence of fatigue and revealed fatigue-induced neck deflection which increased cervical lordosis or kyphosis by 4–5° to 11°, depending on the neck position. This finding and its interpretations render a renewed understanding of muscle fatigue from a more unified motor control perspective as well as profound implications on neck pain and injury prevention.

show abstract

A state-of-the-art integrative approach to studying neck biomechanics in vivo

Cited by 5 publications

References 22 publications

Renewing Iterative Self-labeling Domain Adaptation with Application to the Spine Motion Prediction

Renewing Iterative Self-labeling Domain Adaptation with Application to the Spine Motion Prediction

Inverse kinematics in cervical spine models: Effects of scaling and model degrees of freedom for extension and flexion movements

The deflection of fatigued neck

Contact Info

Product

Resources

About