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Cheng CF, Lin CJ. Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology. SENSORS (BASEL, SWITZERLAND) 2023; 23:2920. [PMID: 36991630 PMCID: PMC10052076 DOI: 10.3390/s23062920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/04/2023] [Accepted: 03/06/2023] [Indexed: 06/19/2023]
Abstract
In recent years, affective computing has emerged as a promising approach to studying user experience, replacing subjective methods that rely on participants' self-evaluation. Affective computing uses biometrics to recognize people's emotional states as they interact with a product. However, the cost of medical-grade biofeedback systems is prohibitive for researchers with limited budgets. An alternative solution is to use consumer-grade devices, which are more affordable. However, these devices require proprietary software to collect data, complicating data processing, synchronization, and integration. Additionally, researchers need multiple computers to control the biofeedback system, increasing equipment costs and complexity. To address these challenges, we developed a low-cost biofeedback platform using inexpensive hardware and open-source libraries. Our software can serve as a system development kit for future studies. We conducted a simple experiment with one participant to validate the platform's effectiveness, using one baseline and two tasks that elicited distinct responses. Our low-cost biofeedback platform provides a reference architecture for researchers with limited budgets who wish to incorporate biometrics into their studies. This platform can be used to develop affective computing models in various domains, including ergonomics, human factors engineering, user experience, human behavioral studies, and human-robot interaction.
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Duarte RP, Marinho FA, Bastos ES, Pinto RJ, Silva PM, Fermino A, Denysyuk HV, Gouveia AJ, Gonçalves NJ, Coelho PJ, Zdravevski E, Lameski P, Tripunovski T, Garcia NM, Pires IM. Extraction of notable points from ECG data: A description of a dataset related to 30-s seated and 30-s stand up. Data Brief 2023; 46:108874. [PMID: 36660441 PMCID: PMC9843242 DOI: 10.1016/j.dib.2022.108874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 12/01/2022] [Accepted: 12/29/2022] [Indexed: 01/07/2023] Open
Abstract
It is increasingly possible to acquire Electrocardiographic data with featured low-cost devices. The proposed dataset will help map different signals for various diseases related to Electrocardiography data. The dataset presented in this paper is related to the acquisition of electrocardiography data during the standing up and seated positions. The data was collected from 219 individuals (112 men, 106 women, and one other) in different environments, but they are in the Covilhã municipality. The dataset includes the 219 recordings and corresponds to the sensors' recordings of a 30 s sitting and a 30 s standing test, which checks to approximately 1 min for each one. This dataset includes 3.7 h (approximately) of recordings for further analysis with data processing techniques and machine learning methods. It will be helpful for the complementary creation of a robust method for identifying the characteristics of individuals related to Electrocardiography signals.
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Affiliation(s)
- Rui Pedro Duarte
- Escola de Ciências e Tecnologia, Universidade de Trás-Os-Montes e Alto Douro, Quinta de Prados, Vila Real 5001-801, Portugal
| | - Francisco Alexandre Marinho
- Escola de Ciências e Tecnologia, Universidade de Trás-Os-Montes e Alto Douro, Quinta de Prados, Vila Real 5001-801, Portugal
| | - Eduarda Sofia Bastos
- Escola de Ciências e Tecnologia, Universidade de Trás-Os-Montes e Alto Douro, Quinta de Prados, Vila Real 5001-801, Portugal
| | - Rui João Pinto
- Escola de Ciências e Tecnologia, Universidade de Trás-Os-Montes e Alto Douro, Quinta de Prados, Vila Real 5001-801, Portugal
| | - Pedro Miguel Silva
- Escola de Ciências e Tecnologia, Universidade de Trás-Os-Montes e Alto Douro, Quinta de Prados, Vila Real 5001-801, Portugal
| | - Alice Fermino
- Computer Science Department, Universidade da Beira Interior, Covilhã 6200-001, Portugal
| | | | - António Jorge Gouveia
- Escola de Ciências e Tecnologia, Universidade de Trás-Os-Montes e Alto Douro, Quinta de Prados, Vila Real 5001-801, Portugal
| | - Norberto Jorge Gonçalves
- Escola de Ciências e Tecnologia, Universidade de Trás-Os-Montes e Alto Douro, Quinta de Prados, Vila Real 5001-801, Portugal
| | - Paulo Jorge Coelho
- School of Technology and Management, Polytechnic of Leiria, Leiria 2411-901, Portugal,Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra), DEEC, Pólo II, Coimbra 3030-290, Portugal
| | - Eftim Zdravevski
- Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia
| | - Petre Lameski
- Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia
| | - Toni Tripunovski
- Institute of Pathophysiology and Nuclear Medicine, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia
| | - Nuno M. Garcia
- Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã 6200-001, Portugal
| | - Ivan Miguel Pires
- Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã 6200-001, Portugal,Corresponding author.
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El-Baz A, Giridharan GA, Shalaby A, Mahmoud AH, Ghazal M. Special Issue "Computer Aided Diagnosis Sensors". SENSORS (BASEL, SWITZERLAND) 2022; 22:8052. [PMID: 36298403 PMCID: PMC9610085 DOI: 10.3390/s22208052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
Sensors used to diagnose, monitor or treat diseases in the medical domain are known as medical sensors [...].
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Affiliation(s)
- Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | | | - Ahmed Shalaby
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ali H. Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
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Krokidis MG, Dimitrakopoulos GN, Vrahatis AG, Tzouvelekis C, Drakoulis D, Papavassileiou F, Exarchos TP, Vlamos P. A Sensor-Based Perspective in Early-Stage Parkinson's Disease: Current State and the Need for Machine Learning Processes. SENSORS (BASEL, SWITZERLAND) 2022; 22:409. [PMID: 35062370 PMCID: PMC8777583 DOI: 10.3390/s22020409] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/02/2021] [Accepted: 01/04/2022] [Indexed: 02/04/2023]
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder associated with dysfunction of dopaminergic neurons in the brain, lack of dopamine and the formation of abnormal Lewy body protein particles. PD is an idiopathic disease of the nervous system, characterized by motor and nonmotor manifestations without a discrete onset of symptoms until a substantial loss of neurons has already occurred, enabling early diagnosis very challenging. Sensor-based platforms have gained much attention in clinical practice screening various biological signals simultaneously and allowing researchers to quickly receive a huge number of biomarkers for diagnostic and prognostic purposes. The integration of machine learning into medical systems provides the potential for optimization of data collection, disease prediction through classification of symptoms and can strongly support data-driven clinical decisions. This work attempts to examine some of the facts and current situation of sensor-based approaches in PD diagnosis and discusses ensemble techniques using sensor-based data for developing machine learning models for personalized risk prediction. Additionally, a biosensing platform combined with clinical data processing and appropriate software is proposed in order to implement a complete diagnostic system for PD monitoring.
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Affiliation(s)
- Marios G. Krokidis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | - Georgios N. Dimitrakopoulos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | - Aristidis G. Vrahatis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | - Christos Tzouvelekis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | | | | | - Themis P. Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | - Panayiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
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