Worker’s physical fatigue classification using neural networks

Escobar-Linero, Elena; Domínguez-Morales, Manuel; Sevillano, José Luis

doi:10.1016/j.eswa.2022.116784

Cited by 28 publications

(11 citation statements)

References 51 publications

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“…With current improvements in hardware performance and communication technologies, advances in the development of wearable IoT devices for health monitoring have been increasing. These include everything from the development of devices which monitor vital signs [ 39 ] to the detection of stress [ 40 ] or habits that put health at risk [ 41 ]. With the use of capacitive insoles and the use of PDMS as a dielectric, given its mechanical qualities, it is hoped to achieve better resolution in the data than those obtained with resistive sensors, allowing one to take advantage of the potential of advanced machine-learning algorithms, which can identify subtle features and are tolerant to noise.…”

Section: Related Workmentioning

confidence: 99%

Smart Shoe Insole Based on Polydimethylsiloxane Composite Capacitive Sensors

Luna-Perejón

Salvador-Domínguez

Perez-Peña

et al. 2023

Sensors

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Nowadays, the study of the gait by analyzing the distribution of plantar pressure is a well-established technique. The use of intelligent insoles allows real-time monitoring of the user. Thus, collecting and analyzing information is a more accurate process than consultations in so-called gait laboratories. Most of the previous published studies consider the composition and operation of these insoles based on resistive sensors. However, the use of capacitive sensors could provide better results, in terms of linear behavior under the pressure exerted. This behavior depends on the properties of the dielectric used. In this work, the design and implementation of an intelligent plantar insole composed of capacitive sensors is proposed. The dielectric used is a polydimethylsiloxane (PDMS)-based composition. The sensorized plantar insole developed achieves its purpose as a tool for collecting pressure in different areas of the sole of the foot. The fundamentals and details of the composition, manufacture, and implementation of the insole and the system used to collect data, as well as the data samples, are shown. Finally, a comparison of the behavior of both insoles, resistive and capacitive sensor-equipped, is made. The prototype presented lays the foundation for the development of a tool to support the diagnosis of gait abnormalities.

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Section: Related Workmentioning

confidence: 99%

Smart Shoe Insole Based on Polydimethylsiloxane Composite Capacitive Sensors

Luna-Perejón

Salvador-Domínguez

Perez-Peña

et al. 2023

Sensors

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“…The creation of models that help the medical professional to give a diagnosis stands out mainly Machine Learning models that can be integrated into the system, sometimes complemented with architectures equipped with resources to apply Fog computing, as well as with the decentralization of processing with computing at the edge, trend that is currently increasing with the optimization of hardware and AI frameworks for model integration and consumption reduction [195]. Additionally, the applications are not restricted to the field of healthcare in the personal context, but also in the workplace [196]. To a lesser extent, we also find the use of IoT to facilitate the remote diagnosis of the patient.…”

Section: Analysis Of Scopes Of Higher Impactmentioning

confidence: 99%

Perspective Chapter: Internet of Things in Healthcare – New Trends, Challenges and Hurdles

Muñoz-Saavedra¹,

Luna-Perejón²,

Civit-Masot³

et al. 2023

Internet of Things - New Trends, Challenges and Hurdles

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Applied to health field, Internet of Things (IoT) systems provides continuous and ubiquitous monitoring and assistance, allowing the creation of valuable tools for diagnosis, health empowerment, and personalized treatment, among others. Advances in these systems follow different approaches, such as the integration of new protocols and standards, combination with artificial intelligence algorithms, application of big data processing methodologies, among others. These new systems and applications also should face different challenges when applying this kind of technology into health areas, such as the management of personal data sensed, integration with electronic health records, make sensing devices comfortable to wear, and achieve an accurate acquisition of the sensed data. The objective of this chapter is to present the state of the art, indicating the most current IoT trends applied to the health field, their contributions, technologies applied, and challenges faced.

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“…Therefore, it is important to keep these limitations in mind when considering the implementation of telemedicine in healthcare, and to look for alternatives or complementary tools, such as artificial intelligence (AI). It is currently used in many areas of healthcare, including drug discovery, genomics, radiology, pathology and prevention, and diagnostic imaging, among others [19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Disease screening using Artificial Intelligence

Fuster-Palà,

Luna-Perejón,

Domínguez-Morales

2024

Preprint

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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.

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Worker’s physical fatigue classification using neural networks

Cited by 28 publications

References 51 publications

Smart Shoe Insole Based on Polydimethylsiloxane Composite Capacitive Sensors

Smart Shoe Insole Based on Polydimethylsiloxane Composite Capacitive Sensors

Perspective Chapter: Internet of Things in Healthcare – New Trends, Challenges and Hurdles

Disease screening using Artificial Intelligence

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