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

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

Bibliographic Details
Main Author: Snyder, Kristian
Language:English
Published: University of Cincinnati / OhioLINK 2020
Subjects:
Artificial Intelligence
activity recognition
back pain
accelerometer
convolutional neural network
deep learning
human activity recognition
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583999458096255
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-ucin15839994580962552021-08-03T07:13:57Z Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting Snyder, Kristian Artificial Intelligence activity recognition back pain accelerometer convolutional neural network deep learning human activity recognition 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. 2020-10-05 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583999458096255 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583999458096255 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Artificial Intelligence
activity recognition
back pain
accelerometer
convolutional neural network
deep learning
human activity recognition
spellingShingle Artificial Intelligence
activity recognition
back pain
accelerometer
convolutional neural network
deep learning
human activity recognition
Snyder, Kristian
Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting
author Snyder, Kristian
author_facet Snyder, Kristian
author_sort Snyder, Kristian
title Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting
title_short Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting
title_full Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting
title_fullStr Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting
title_full_unstemmed Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting
title_sort utilizing convolutional neural networks for specialized activity recognition: classifying lower back pain risk prediction during manual lifting
publisher University of Cincinnati / OhioLINK
publishDate 2020
url http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583999458096255
work_keys_str_mv AT snyderkristian utilizingconvolutionalneuralnetworksforspecializedactivityrecognitionclassifyinglowerbackpainriskpredictionduringmanuallifting
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