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Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting

Snyder, Kristian
http://orcid.org/0000-0002-3448-2381
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583999458096255

Abstract Details

2020, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
Classification of specialized human activity datasets utilizing methods not requiring manual feature extraction is an underserved area of research in the field of human activity recognition (HAR). In this thesis, we present a convolutional neural network (CNN)-based method to classify a dataset consisting of subjects lifting an object from various positions relative to their bodies, labeled by the level of back pain risk attributed to the action. Specific improvements over other CNN-based models for both general and activity-based purposes include the use of average pooling and dropout layers. Methods to reshape accelerometer and gyroscope sensor data are also presented to encourage the model’s use with other datasets. When developing the model, a dataset previously developed by the National Institute for Occupational Safety and Health (NIOSH) was used. It consists of 720 total trials of accelerometer and gyroscope data from subjects lifting an object at various relative distances from the body. In testing, 90.6% accuracy was achieved on the NIOSH lifting dataset, a significant improvement over other models tested. Saliency results are also presented to investigate underlying feature extraction and justify the results collected.
Rashmi Jha, Ph.D. (Committee Chair)
Ming-Lun Lu, Ph.D. (Committee Member)
Boyang Wang, Ph.D. (Committee Member)
77 p.
Artificial Intelligence
activity recognition; back pain; accelerometer; convolutional neural network; deep learning; human activity recognition

Recommended Citations

Citations

  • Snyder, K. (2020). Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583999458096255

    APA Style (7th edition)

  • Snyder, Kristian. Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting. 2020. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583999458096255.

    MLA Style (8th edition)

  • Snyder, Kristian. "Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting." Master's thesis, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583999458096255

    Chicago Manual of Style (17th edition)

ucin1583999458096255
238
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This open access ETD is published by University of Cincinnati and OhioLINK.