- -

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 completo del ítem

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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/168610

Ficheros en el ítem

Thumbnail
Nombre: Araujo;Rncón;Julan ...
Tamaño: 2.705Mb
Formato: PDF
Descripción: Versión editorial
Abrir/Preview

Metadatos del ítem

Título: A Low-Cost Cognitive Assistant
Autor: Araujo, Angelo Rincón Arango, Jaime Andrés Julian Inglada, Vicente Javier Novais, Paulo Carrascosa Casamayor, Carlos
Entidad UPV: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Fecha difusión:
Resumen:
[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 ...[+]
Palabras clave: Cognitive assistants , Aging , Emotion recognition
Derechos de uso: Reconocimiento (by)
Fuente:
Electronics. (eissn: 2079-9292 )
DOI: 10.3390/electronics9020310
Editorial:
MDPI AG
Versión del editor: https://doi.org/10.3390/electronics9020310
Código del Proyecto:
info:eu-repo/grantAgreement/FCT/SFRH/SFRH%2FBPD%2F102696%2F2014/PT/
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/
info:eu-repo/grantAgreement/GVA//PROMETEO%2F2018%2F002/ES/TECNOLOGIES PER ORGANITZACIONS HUMANES EMOCIONALS/
Agradecimientos:
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 ...[+]
Tipo: Artículo

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

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

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 [+]
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

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

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

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

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

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

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

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

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

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

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

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

Costa, A., Martinez-Martin, E., Cazorla, M., & Julian, V. (2018). PHAROS—PHysical Assistant RObot System. Sensors, 18(8), 2633. doi:10.3390/s18082633

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

CoMEhttp://come-aal.eu/

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[-]

recommendations

 

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

Mostrar el registro completo del ítem