scholarly journals Reducing lumbar spine flexion using real-time biofeedback during patient handling tasks

Work ◽  
2020 ◽  
Vol 66 (1) ◽  
pp. 41-51 ◽  
Author(s):  
Mohammadhasan Owlia ◽  
Megan Kamachi ◽  
Tilak Dutta
2005 ◽  
Vol 5 (1) ◽  
pp. 85-94 ◽  
Author(s):  
Eric H. Ledet ◽  
Michael P. Tymeson ◽  
Darryl J. DiRisio ◽  
Benjamin Cohen ◽  
Richard L. Uhl

2000 ◽  
Vol 31 (2) ◽  
pp. 185-200 ◽  
Author(s):  
Wendy Elford ◽  
Leon Straker ◽  
Geoffrey Strauss

Author(s):  
Christofer Schröder ◽  
Albert Nienhaus

Lifting or carrying loads or working while the trunk is in a bent position are well established risk factors for the development of disc disease of the lumbar spine (LDD). Patient handling is associated with certain hazardous activities, which can result in exposure to heavy loads and high pressure for the discs of the lumbar spine of the nurses performing these tasks. The purpose of this review was to examine the occurrence of work-related LDD among health personnel (HP) with occupational exposure to patient handling activities in comparison to un-exposed workers. A systematic literature search was conducted using the following databases: PubMed, CINAHL, Scopus, and Web of Science. A meta-analysis of odds ratios (OR) was conducted by stratifying for various factors. Five studies reported a higher prevalence for LDD among nurses and geriatric nurses (11.3–96.3%) compared to all controls (3.78–76.47%). Results of the meta-analysis showed a significantly increased OR for LDD among HP compared to all controls (OR 2.45; 95% confidence interval (CI) 1.41, 4.26). In particular, the results of this review suggest that nurses have a higher probability of developing disc herniation than office workers.


Digital Twin ◽  
2021 ◽  
Vol 1 ◽  
pp. 8
Author(s):  
Xiwang He ◽  
Yiming Qiu ◽  
Xiaonan Lai ◽  
Zhonghai Li ◽  
Liming Shu ◽  
...  

Background: With significant advancement and demand for digital transformation, the digital twin has been gaining increasing attention as it is capable of establishing real-time mapping between physical space and virtual space. In this work, a shape-performance integrated digital twin solution is presented to predict the real-time biomechanics of the lumbar spine during human movement. Methods: A finite element model (FEM) of the lumbar spine was firstly developed using computed tomography (CT) and constrained by the body movement which was calculated by the inverse kinematics algorithm. The Gaussian process regression was utilized to train the predicted results and create the digital twin of the lumbar spine in real-time. Finally, a three-dimensional virtual reality system was developed using Unity3D to display and record the real-time biomechanics performance of the lumbar spine during body movement. Results: The evaluation results presented an agreement (R-squared > 0.8) between the real-time prediction from digital twin and offline FEM prediction. Conclusions: This approach provides an effective method of real-time planning and warning in spine rehabilitation.


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