- -

A Low-Cost Cognitive Assistant

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

A Low-Cost Cognitive Assistant

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Araujo, Angelo es_ES
dc.contributor.author Rincón Arango, Jaime Andrés es_ES
dc.contributor.author Julian Inglada, Vicente Javier es_ES
dc.contributor.author Novais, Paulo es_ES
dc.contributor.author Carrascosa Casamayor, Carlos es_ES
dc.date.accessioned 2021-07-01T03:32:46Z
dc.date.available 2021-07-01T03:32:46Z
dc.date.issued 2020-02 es_ES
dc.identifier.uri http://hdl.handle.net/10251/168610
dc.description.abstract [EN] In this paper, we present in depth the hardware components of a low-cost cognitive assistant. The aim is to detect the performance and the emotional state that elderly people present when performing exercises. Physical and cognitive exercises are a proven way of keeping elderly people active, healthy, and happy. Our goal is to bring to people that are at their homes (or in unsupervised places) an assistant that motivates them to perform exercises and, concurrently, monitor them, observing their physical and emotional responses. We focus on the hardware parts and the deep learning models so that they can be reproduced by others. The platform is being tested at an elderly people care facility, and validation is in process. es_ES
dc.description.sponsorship This work was partly supported by the FCT (Fundacao para a Ciencia e Tecnologia) through the Post-Doc scholarship SFRH/BPD/102696/2014 (A. Costa), by the Generalitat Valenciana (PROMETEO/2018/002), and by the Spanish Government (RTI2018-095390-B-C31). es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Electronics es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Cognitive assistants es_ES
dc.subject Aging es_ES
dc.subject Emotion recognition es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title A Low-Cost Cognitive Assistant es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/electronics9020310 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/FCT/SFRH/SFRH%2FBPD%2F102696%2F2014/PT/
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095390-B-C31/ES/HACIA UNA MOVILIDAD INTELIGENTE Y SOSTENIBLE SOPORTADA POR SISTEMAS MULTI-AGENTES Y EDGE COMPUTING/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//PROMETEO%2F2018%2F002/ES/TECNOLOGIES PER ORGANITZACIONS HUMANES EMOCIONALS/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Araujo, A.; Rincón Arango, JA.; Julian Inglada, VJ.; Novais, P.; Carrascosa Casamayor, C. (2020). A Low-Cost Cognitive Assistant. Electronics. 9(2):1-19. https://doi.org/10.3390/electronics9020310 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/electronics9020310 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 19 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 9 es_ES
dc.description.issue 2 es_ES
dc.identifier.eissn 2079-9292 es_ES
dc.relation.pasarela S\402625 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder Fundação para a Ciência e a Tecnologia, Portugal es_ES
dc.description.references Licher, S., Darweesh, S. K. L., Wolters, F. J., Fani, L., Heshmatollah, A., Mutlu, U., … Ikram, M. A. (2018). Lifetime risk of common neurological diseases in the elderly population. Journal of Neurology, Neurosurgery & Psychiatry, 90(2), 148-156. doi:10.1136/jnnp-2018-318650 es_ES
dc.description.references Jaul, E., & Barron, J. (2017). Age-Related Diseases and Clinical and Public Health Implications for the 85 Years Old and Over Population. Frontiers in Public Health, 5. doi:10.3389/fpubh.2017.00335 es_ES
dc.description.references Brasure, M., Desai, P., Davila, H., Nelson, V. A., Calvert, C., Jutkowitz, E., … Kane, R. L. (2017). Physical Activity Interventions in Preventing Cognitive Decline and Alzheimer-Type Dementia. Annals of Internal Medicine, 168(1), 30. doi:10.7326/m17-1528 es_ES
dc.description.references Iuliano, E., di Cagno, A., Cristofano, A., Angiolillo, A., D’Aversa, R., … Di Costanzo, A. (2019). Physical exercise for prevention of dementia (EPD) study: background, design and methods. BMC Public Health, 19(1). doi:10.1186/s12889-019-7027-3 es_ES
dc.description.references Müllers, P., Taubert, M., & Müller, N. G. (2019). Physical Exercise as Personalized Medicine for Dementia Prevention? Frontiers in Physiology, 10. doi:10.3389/fphys.2019.00672 es_ES
dc.description.references Pérez-Fuentes, M. del C., Gázquez Linares, J. J., Ruiz Fernández, M. D., & Molero Jurado, M. del M. (2017). Inventory of Overburden in Alzheimer’s Patient Family Caregivers with no Specialized Training. International Journal of Clinical and Health Psychology, 17(1), 56-64. doi:10.1016/j.ijchp.2016.09.004 es_ES
dc.description.references Berglund, E., Lytsy, P., & Westerling, R. (2015). Health and wellbeing in informal caregivers and non-caregivers: a comparative cross-sectional study of the Swedish general population. Health and Quality of Life Outcomes, 13(1). doi:10.1186/s12955-015-0309-2 es_ES
dc.description.references Peña-Longobardo, L. M., & Oliva-Moreno, J. (2014). Caregiver Burden in Alzheimer’s Disease Patients in Spain. Journal of Alzheimer’s Disease, 43(4), 1293-1302. doi:10.3233/jad-141374 es_ES
dc.description.references Hoefman, R. J., Meulenkamp, T. M., & De Jong, J. D. (2017). Who is responsible for providing care? Investigating the role of care tasks and past experiences in a cross-sectional survey in the Netherlands. BMC Health Services Research, 17(1). doi:10.1186/s12913-017-2435-5 es_ES
dc.description.references Pearson, C. F., Quinn, C. C., Loganathan, S., Datta, A. R., Mace, B. B., & Grabowski, D. C. (2019). The Forgotten Middle: Many Middle-Income Seniors Will Have Insufficient Resources For Housing And Health Care. Health Affairs, 38(5), 10.1377/hlthaff. doi:10.1377/hlthaff.2018.05233 es_ES
dc.description.references Costa, A., Novais, P., Julian, V., & Nalepa, G. J. (2018). Cognitive assistants. International Journal of Human-Computer Studies, 117, 1-3. doi:10.1016/j.ijhcs.2018.05.008 es_ES
dc.description.references Martinez-Martin, E., & del Pobil, A. P. (2017). Personal Robot Assistants for Elderly Care: An Overview. Personal Assistants: Emerging Computational Technologies, 77-91. doi:10.1007/978-3-319-62530-0_5 es_ES
dc.description.references Costa, A., Martinez-Martin, E., Cazorla, M., & Julian, V. (2018). PHAROS—PHysical Assistant RObot System. Sensors, 18(8), 2633. doi:10.3390/s18082633 es_ES
dc.description.references Castillo, J. C., Álvarez-Fernández, D., Alonso-Martín, F., Marques-Villarroya, S., & Salichs, M. A. (2018). Social Robotics in Therapy of Apraxia of Speech. Journal of Healthcare Engineering, 2018, 1-11. doi:10.1155/2018/7075290 es_ES
dc.description.references CoMEhttp://come-aal.eu/ es_ES
dc.description.references Costa, A., Rincon, J. A., Carrascosa, C., Novais, P., & Julian, V. (2018). Activities suggestion based on emotions in AAL environments. Artificial Intelligence in Medicine, 86, 9-19. doi:10.1016/j.artmed.2018.01.002 es_ES
dc.description.references Costa, A., Novais, P., & Simoes, R. (2014). A Caregiver Support Platform within the Scope of an Ambient Assisted Living Ecosystem. Sensors, 14(3), 5654-5676. doi:10.3390/s140305654 es_ES
dc.description.references Costa, Â., Heras, S., Palanca, J., Jordán, J., Novais, P., & Julian, V. (2017). Using Argumentation Schemes for a Persuasive Cognitive Assistant System. Lecture Notes in Computer Science, 538-546. doi:10.1007/978-3-319-59294-7_43 es_ES
dc.description.references Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. (2019). Deep learning for sensor-based activity recognition: A survey. Pattern Recognition Letters, 119, 3-11. doi:10.1016/j.patrec.2018.02.010 es_ES
dc.description.references Nweke, H. F., Teh, Y. W., Al-garadi Mohammed Ali, & Alo, U. R. (2018). Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Systems with Applications, 105, 233-261. doi:10.1016/j.eswa.2018.03.056 es_ES
dc.description.references Martinez-Martin, E., & Cazorla, M. (2019). A Socially Assistive Robot for Elderly Exercise Promotion. IEEE Access, 7, 75515-75529. doi:10.1109/access.2019.2921257 es_ES
dc.description.references Martinez-Martin, E., & Cazorla, M. (2019). Rehabilitation Technology: Assistance from Hospital to Home. Computational Intelligence and Neuroscience, 2019, 1-8. doi:10.1155/2019/1431509 es_ES
dc.description.references Cruz, E., Escalona, F., Bauer, Z., Cazorla, M., García-Rodríguez, J., Martinez-Martin, E., … Gomez-Donoso, F. (2018). Geoffrey: An Automated Schedule System on a Social Robot for the Intellectually Challenged. Computational Intelligence and Neuroscience, 2018, 1-17. doi:10.1155/2018/4350272 es_ES
dc.description.references Vepakomma, P., De, D., Das, S. K., & Bhansali, S. (2015). A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities. 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN). doi:10.1109/bsn.2015.7299406 es_ES
dc.description.references Cao, L., Wang, Y., Zhang, B., Jin, Q., & Vasilakos, A. V. (2018). GCHAR: An efficient Group-based Context—aware human activity recognition on smartphone. Journal of Parallel and Distributed Computing, 118, 67-80. doi:10.1016/j.jpdc.2017.05.007 es_ES
dc.description.references Marechal, C., Mikołajewski, D., Tyburek, K., Prokopowicz, P., Bougueroua, L., Ancourt, C., & Węgrzyn-Wolska, K. (2019). Survey on AI-Based Multimodal Methods for Emotion Detection. High-Performance Modelling and Simulation for Big Data Applications, 307-324. doi:10.1007/978-3-030-16272-6_11 es_ES
dc.description.references Brás, S., Ferreira, J. H. T., Soares, S. C., & Pinho, A. J. (2018). Biometric and Emotion Identification: An ECG Compression Based Method. Frontiers in Psychology, 9. doi:10.3389/fpsyg.2018.00467 es_ES
dc.description.references Goshvarpour, A., Abbasi, A., & Goshvarpour, A. (2017). An accurate emotion recognition system using ECG and GSR signals and matching pursuit method. Biomedical Journal, 40(6), 355-368. doi:10.1016/j.bj.2017.11.001 es_ES
dc.description.references Naji, M., Firoozabadi, M., & Azadfallah, P. (2014). A new information fusion approach for recognition of music-induced emotions. IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). doi:10.1109/bhi.2014.6864340 es_ES
dc.description.references Naji, M., Firoozabadi, M., & Azadfallah, P. (2013). Emotion classification during music listening from forehead biosignals. Signal, Image and Video Processing, 9(6), 1365-1375. doi:10.1007/s11760-013-0591-6 es_ES
dc.description.references Seoane, F., Mohino-Herranz, I., Ferreira, J., Alvarez, L., Buendia, R., Ayllón, D., … Gil-Pita, R. (2014). Wearable Biomedical Measurement Systems for Assessment of Mental Stress of Combatants in Real Time. Sensors, 14(4), 7120-7141. doi:10.3390/s140407120 es_ES
dc.description.references Diaz, K. M., Krupka, D. J., Chang, M. J., Peacock, J., Ma, Y., Goldsmith, J., … Davidson, K. W. (2015). Fitbit®: An accurate and reliable device for wireless physical activity tracking. International Journal of Cardiology, 185, 138-140. doi:10.1016/j.ijcard.2015.03.038 es_ES
dc.description.references Falter, M., Budts, W., Goetschalckx, K., Cornelissen, V., & Buys, R. (2019). Accuracy of Apple Watch Measurements for Heart Rate and Energy Expenditure in Patients With Cardiovascular Disease: Cross-Sectional Study. JMIR mHealth and uHealth, 7(3), e11889. doi:10.2196/11889 es_ES
dc.description.references Rincon, J. A., Julian, V., Carrascosa, C., Costa, A., & Novais, P. (2018). Detecting emotions through non-invasive wearables. Logic Journal of the IGPL. doi:10.1093/jigpal/jzy025 es_ES
dc.description.references Rincon, J. A., Costa, A., Carrascosa, C., Novais, P., & Julian, V. (2019). EMERALD—Exercise Monitoring Emotional Assistant. Sensors, 19(8), 1953. doi:10.3390/s19081953 es_ES
dc.description.references Kannus, P., Sievänen, H., Palvanen, M., Järvinen, T., & Parkkari, J. (2005). Prevention of falls and consequent injuries in elderly people. The Lancet, 366(9500), 1885-1893. doi:10.1016/s0140-6736(05)67604-0 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem