A deep learning approach for lower back-pain risk prediction during manual lifting

@article{Snyder2021ADL,
  title={A deep learning approach for lower back-pain risk prediction during manual lifting},
  author={K. Snyder and T. Brennan and M. Lu and Rashmi Jha and M. Barim and Marie Hayden and D. Werren},
  journal={PLoS ONE},
  year={2021},
  volume={16}
}
  • K. Snyder, T. Brennan, +4 authors D. Werren
  • Published 2021
  • Computer Science, Engineering, Mathematics, Medicine
  • PLoS ONE
  • Occupationally-induced back pain is a leading cause of reduced productivity in industry. Detecting when a worker is lifting incorrectly and at increased risk of back injury presents significant possible benefits. These include increased quality of life for the worker due to lower rates of back injury and fewer workers’ compensation claims and missed time for the employer. However, recognizing lifting risk provides a challenge due to typically small datasets and subtle underlying features in… CONTINUE READING

    References

    SHOWING 1-10 OF 43 REFERENCES
    Deep, Convolutional, and Recurrent Models for Human Activity Recognition Using Wearables
    • 433
    • PDF
    Efficacy of the Revised NIOSH Lifting Equation to Predict Risk of Low Back Pain Due to Manual Lifting: Expanded Cross-Sectional Analysis
    • 60
    Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
    • 1,004
    • PDF
    Activity Recognition from User-Annotated Acceleration Data
    • 2,985
    • PDF
    Physical Activities Monitoring Using Wearable Acceleration Sensors Attached to the Body
    • 58
    • PDF
    A systematic review of the global prevalence of low back pain.
    • 1,496
    Max-pooling convolutional neural networks for vision-based hand gesture recognition
    • 323
    • PDF