Applying Wearable Technology and a Deep Learning Model to Predict Occupational Physical Activities

Many workers who engage in manual material handling (MMH) jobs experience high physical demands that are associated with work-related musculoskeletal disorders (WMSDs). Quantifying the physical demands of a job is important for identifying high risk jobs and is a legal requirement in the United States for hiring and return to work following injury. Currently, most physical demand analyses (PDAs) are performed by experts using observational and semi-quantitative methods. The lack of accuracy and reliability of these methods can be problematic, particularly when identifying restrictions during the return-to-work process. Further, when a worker does return-to-work on modified duty, there is no way to track compliance to work restrictions conflating the effectiveness of the work restrictions versus adherence to them. To address this, we applied a deep learning model to data from eight inertial measurement units (IMUs) to predict 15 occupational physical activities. Overall, a 95% accuracy was reached for predicting isolated occupational physical activities. However, when applied to more complex tasks that combined occupational physical activities (OPAs), accuracy varied widely (0–95%). More work is needed to accurately predict OPAs when combined into simulated work tasks.

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Prediction of Occupational Physical Activities using Inertial Measurement Units and Deep Learning Models

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641206 ◽

2020 ◽

Vol 64 (1) ◽

pp. 879-881

Author(s):

Yishu Yan ◽

Hao Fan ◽

Yibin Li ◽

Elias Hoeglinger ◽

Alexander Wiesinger ◽

...

Keyword(s):

Deep Learning ◽

Material Handling ◽

Quantitative Methods ◽

Physical Activities ◽

Inertial Measurement Units ◽

Learning Models ◽

Physical Demands ◽

Inertial Measurement ◽

Work Related ◽

Measurement Units

Many workers engaged in manual material handling (MMH) jobs experience high physical demands that are associated with work-related musculoskeletal disorders (WMSD). Quantifying the physical demands of a job is an important legal requirement in the US that is used by human resources in the job hiring process. Most physical demands analysis (PDA) are performed using observational and semi-quantitative methods. The lack of accuracy and reliability of these methods can create problems when assigning acceptable tasks to an injured worker. In this study, various deep learning models were applied to data from eight inertial measurement units (IMUs) to predict 15 occupational physical activities (OPA). Overall, a 95% accuracy was reached by convolutional neural network (CNN) for predicting occupational physical activities when performed in isolation. More work is needed to estimate the accuracy of the model when OPA elements are combined into a more complex task.

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Screening patents of ICT in construction using deep learning and NLP techniques

Engineering Construction & Architectural Management ◽

10.1108/ecam-09-2019-0480 ◽

2020 ◽

Vol 27 (8) ◽

pp. 1891-1912

Author(s):

Hengqin Wu ◽

Geoffrey Shen ◽

Xue Lin ◽

Minglei Li ◽

Boyu Zhang ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Fundamental Problem ◽

The United States ◽

Learning Model ◽

Content Type ◽

Patent Retrieval ◽

Part Of Speech ◽

Textual Data ◽

Deep Learning Model

PurposeThis study proposes an approach to solve the fundamental problem in using query-based methods (i.e. searching engines and patent retrieval tools) to screen patents of information and communication technology in construction (ICTC). The fundamental problem is that ICTC incorporates various techniques and thus cannot be simply represented by man-made queries. To investigate this concern, this study develops a binary classifier by utilizing deep learning and NLP techniques to automatically identify whether a patent is relevant to ICTC, thus accurately screening a corpus of ICTC patents.Design/methodology/approachThis study employs NLP techniques to convert the textual data of patents into numerical vectors. Then, a supervised deep learning model is developed to learn the relations between the input vectors and outputs.FindingsThe validation results indicate that (1) the proposed approach has a better performance in screening ICTC patents than traditional machine learning methods; (2) besides the United States Patent and Trademark Office (USPTO) that provides structured and well-written patents, the approach could also accurately screen patents form Derwent Innovations Index (DIX), in which patents are written in different genres.Practical implicationsThis study contributes a specific collection for ICTC patents, which is not provided by the patent offices.Social implicationsThe proposed approach contributes an alternative manner in gathering a corpus of patents for domains like ICTC that neither exists as a searchable classification in patent offices, nor is accurately represented by man-made queries.Originality/valueA deep learning model with two layers of neurons is developed to learn the non-linear relations between the input features and outputs providing better performance than traditional machine learning models. This study uses advanced NLP techniques lemmatization and part-of-speech POS to process textual data of ICTC patents. This study contributes specific collection for ICTC patents which is not provided by the patent offices.

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Early Prediction of Alzheimer’s Disease with a Multimodal Multitask Deep Learning Model

Journal of Student Research ◽

10.47611/jsrhs.v10i1.1366 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Nitin Seshadri ◽

Dr. Serena McCalla ◽

Rishi Shah

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Clinical Setting ◽

The United States ◽

Learning Model ◽

Disease Assessment ◽

Process Data ◽

Clinical Images ◽

Deep Learning Model

Alzheimer’s disease (AD) is the sixth leading cause of death in the United States and the most common neurodegenerative disease in adults over 65. Early-stage AD is often misinterpreted as normal cognitive aging because it may not cause adverse symptoms or visible behavioral changes for up to 20 years. Machine learning has been used to avoid misinterpretation of data and more accurately predict the onset of AD. This study aims to use the data typically available in a clinical setting to predict the onset of AD while maintaining a high level of accuracy. This study proposes a deep learning model that uses multimodal input data and performs multitask classification to predict AD diagnosis and scores of two commonly used cognitive assessments: Alzheimer’s Disease Assessment Scale (ADAS) and Mini-Mental State Examination (MMSE). The model was validated using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset of 1737 patients. The current model achieved a greater accuracy in predicting AD diagnosis and a lower error in predicting ADAS and MMSE scores than existing state-of-the-art models. This model can be applied to the clinical setting so that accurate diagnosis can be achieved, and appropriate action can be taken. Future investigations could include using a convolutional neural network (CNN) to process data from clinical images directly or training and validating the model with other clinical datasets to further improve its accuracy.

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Deep Learning Model Selection of Suboptimal Complexity

Автоматика и телемеханика ◽

10.31857/s000523100001252-1 ◽

2018 ◽

pp. 129-147

Author(s):

Oleg Bakhteev ◽

◽

Vadim Strijov ◽

Keyword(s):

Deep Learning ◽

Model Selection ◽

Learning Model ◽

Deep Learning Model ◽

Selection Of

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Improving Sentiment Analysis using Hybrid Deep Learning Model

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190328200012 ◽

2020 ◽

Vol 13 (4) ◽

pp. 627-640 ◽

Cited By ~ 1

Author(s):

Avinash Chandra Pandey ◽

Dharmveer Singh Rajpoot

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sentiment Analysis ◽

Classification Accuracy ◽

Short Term Memory ◽

Computational Cost ◽

Extraction Process ◽

Learning Model ◽

Sentiment Classification ◽

Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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