Designing and evaluating a wearable device for affective state level classification using machine learning techniques

Muñoz-Saavedra, Luis; Escobar-Linero, Elena; Miró-Amarante, Lourdes; Gómez-Millán, María Rocío Bohórquez; Domínguez-Morales, Manuel

doi:10.1016/j.eswa.2023.119577

Cited by 5 publications

(3 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As future work, comparative evaluations with the performances of capsule network-based methods can be performed because a capsule network can preserve spatial relationships of learned features, and have been proposed recently for image classifications [9] . Other future work may be integrating these classification systems into slow-cost embedded systems and study its importance, as has been proposed in previous works [6] , [21] , [19] .…”

Section: Discussionmentioning

confidence: 99%

Classification of skin blemishes with cell phone images using deep learning techniques

Rangel-Ramos,

Luna-Perejón,

Civit

et al. 2024

Heliyon

Self Cite

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Classification of skin blemishes with cell phone images using deep learning techniques

Rangel-Ramos,

Luna-Perejón,

Civit

et al. 2024

Heliyon

Self Cite

View full text Add to dashboard Cite

“…Gupta Swadha studied the role of SVM algorithm model in device state detection [11]; Ma Yuchun used improved XGBoost to conduct real-time observation and record of device state [12]; Xu Huibo used BP neural network algorithm to binary classify device state, and output two states of "normal" and "abnormal" [13]. Based on previous studies, this project summarized the application of various machine learning algorithms in device state detection, pre-processed the data, divided the data set, used various machine learning regression models to train the data, calculated the evaluation parameters of the machine learning regression model, such as MSE, RMSE, MAE, MAPE, R² , etc., and compared the effects of each model.…”

Section: Introductionmentioning

confidence: 99%

Comparison and analysis of accuracy of various machine learning algorithms in abnormal state monitoring of internet of things devices

Zhang

2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

With the development of Internet of Things technology, more and more devices are connected to the Internet, including not only traditional computers, mobile phones and other smart terminal devices, but also various sensor devices. These sensor devices can collect a variety of environmental information and physical quantities, such as temperature, humidity, air pressure, light intensity, vibration, etc. These data have the characteristics of real-time, scale and diversity, and need to be processed and analyzed by appropriate algorithms. On the basis of previous studies, this project summarized the application of various machine learning algorithms in device state detection, compared the differences of various machine learning algorithms in sensor device detection and made comparative analysis, calculated the evaluation parameters of MSE, RMSE, MAE, MAPE, R² and other aspects of the machine learning regression model. Compare the effects of various regression models for better monitoring and prediction of equipment status. Through the analysis of a large number of historical data, different equipment state models can be established, and these models can be used to monitor and predict the current equipment state. This can effectively avoid production line downtime or other losses caused by equipment failures or abnormalities. At the same time, through the in-depth analysis of historical data, we can find some potential problems and take corresponding measures to prevent them. This project aims to summarize the application of various machine learning algorithms in device status detection, compare and contrast the differences of various machine learning algorithms in sensor device detection, realize efficient processing and analysis of sensor data, calculate MSE, RMSE, MAE, MAPE, R² and other evaluation parameters, and evaluate and compare each model. To provide more accurate, reliable and efficient equipment condition monitoring and forecasting services for enterprises and individuals.

show abstract

“…On the one hand, telemedicine can be used for the evaluation and follow-up of patients with chronic conditions, such as diabetes, arterial hypertension, and chronic obstructive pulmonary disease [13,14]. Telemedicine has also been shown to be effective in the early detection of mental illnesses such as depression, anxiety, and bipolar disorder [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Disease screening using Artificial Intelligence

Fuster-Palà,

Luna-Perejón,

Domínguez-Morales

2024

Preprint

Self Cite

View full text Add to dashboard Cite

This work presents a disease detection classifier based on symptoms encoded by their severity. This model is presented as part of the solution to the saturation of the healthcare system, aiding in the initial screening stage. An open-source dataset was used, which underwent pre-processing and served as the data source to train and test various machine learning models, including SVM, Random Forests, KNN, and ANNs. A 3-phase optimization process is developed to obtain the best classifier: first, the dataset is pre-processed; secondly, a grid search is performed with several hypermarameters variations to each classifier; and, finally, the best models obtained are subjected to an additional filtering processes. The best-results model, selected based on the performance and the execution time, is a KNN with 2 neighbors, which achieves an accuracy and F1 score of over 98\%. These results demonstrate the effectiveness and improvement of the evaluated models compared to previous studies, particularly in terms of accuracy. Although the ANN model has a longer execution time compared to KNN, it is retained in the work due to its potential to handle more complex datasets in a real clinical context.

show abstract

Designing and evaluating a wearable device for affective state level classification using machine learning techniques

Cited by 5 publications

References 32 publications

Classification of skin blemishes with cell phone images using deep learning techniques

Classification of skin blemishes with cell phone images using deep learning techniques

Comparison and analysis of accuracy of various machine learning algorithms in abnormal state monitoring of internet of things devices

Disease screening using Artificial Intelligence

Contact Info

Product

Resources

About