Abstract
The application of medical big data is increasingly popular in healthcare services and clinical research to meet healthcare demands. We designed a spinal posture recognition device with the effective acquisition of surface EMG (sEMG) signal, meanwhile innovatively took cloud storage for visualization and storage, and used BP neural network for classification of different spinal postures. After experiments, the hardware can collect sEMG signals with a signal-to-noise ratio of about 70 dB, and the method can effectively distinguish different spinal postures with a correct rate of greater than 65%. The device is meant for portable big data for scoliosis detection and spinal rehabilitation evaluation.
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Acknowledgments
This work presented in the manuscript was sponsored by Talent introduction research support of Nanjing Institute of Technology (YKJ202022) and Innovation and Entrepreneurship Training Program for College Students in Jiangsu Province (202111276057Y).
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Wu, Y. et al. (2022). Spinal Posture Recognition Device Using Cloud Storage and BP Neural Network Approach Based on Surface Electromyographic Signal. In: Tian, Y., Ma, T., Khan, M.K., Sheng, V.S., Pan, Z. (eds) Big Data and Security. ICBDS 2021. Communications in Computer and Information Science, vol 1563. Springer, Singapore. https://doi.org/10.1007/978-981-19-0852-1_40
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DOI: https://doi.org/10.1007/978-981-19-0852-1_40
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