- -

EMERALD- Exercise Monitoring Emotional Assistant

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

EMERALD- Exercise Monitoring Emotional Assistant

Mostrar el registro completo del ítem

Rincon, J.; Araujo, A.; Carrascosa Casamayor, C.; Novais, P.; Julian Inglada, VJ. (2019). EMERALD- Exercise Monitoring Emotional Assistant. Sensors. 19(8):1-21. https://doi.org/10.3390/s19081953

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/140227

Ficheros en el ítem

Metadatos del ítem

Título: EMERALD- Exercise Monitoring Emotional Assistant
Autor: Rincon, J.A. Araujo, Angelo Carrascosa Casamayor, Carlos Novais, Paulo Julian Inglada, Vicente Javier
Entidad UPV: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Fecha difusión:
Resumen:
[EN] The increase in the elderly population in today's society entails the need for new policies to maintain an adequate level of care without excessively increasing social spending. One of the possible options is to promote ...[+]
Palabras clave: Cognitive assistant , Wearable , Emotion detection , Signal processing , Elderly well-being
Derechos de uso: Reconocimiento (by)
Fuente:
Sensors. (eissn: 1424-8220 )
DOI: 10.3390/s19081953
Editorial:
MDPI AG
Versión del editor: https://doi.org/10.3390/s19081953
Código del Proyecto:
info:eu-repo/grantAgreement/FCT/5876/147280/PT/ALGORITMI Research Centre/
info:eu-repo/grantAgreement/MINECO//TIN2015-65515-C4-1-R/ES/ARQUITECTURA PERSUASIVA PARA EL USO SOSTENIBLE E INTELIGENTE DE VEHICULOS EN FLOTAS URBANAS/
info:eu-repo/grantAgreement/EC/H2020/690874/EU/RISEWISE -RISE Women with disabilities In Social Engagement/
info:eu-repo/grantAgreement/FCT/SFRH/SFRH%2FBPD%2F102696%2F2014/PT/
Agradecimientos:
This research was partially funded by the Fundacao para a Ciencia e Tecnologia (FCT) within the projects UID/CEC/00319/2019 and Post-Doc Grant SFRH/BPD/102696/2014 (Angelo Costa). This work is also partially funded by the ...[+]
Tipo: Artículo

References

World Population Prospects: the 2017 Revision, Key Findings and Advance Tables. Report, United Nations, Department of Economic and Social Affairs, Population Divisionhttps://esa.un.org/unpd/wpp/Publications/Files/WPP2017_KeyFindings.pdf

The Economic Consequences of Ageing Populations. Report 138, European Economy. Economic Papershttp://ec.europa.eu/economyfinance/publications/pages/publication11151en.pdf

World Alzheimer’s Report 2015: The Global Impact of Dementia, an Analysis of Prevalence, Incidence, Cost and Trends. Technical Report, Alzheimer’s Disease Internationalhttps://www.alz.co.uk/research/WorldAlzheimerReport2015.pdf [+]
World Population Prospects: the 2017 Revision, Key Findings and Advance Tables. Report, United Nations, Department of Economic and Social Affairs, Population Divisionhttps://esa.un.org/unpd/wpp/Publications/Files/WPP2017_KeyFindings.pdf

The Economic Consequences of Ageing Populations. Report 138, European Economy. Economic Papershttp://ec.europa.eu/economyfinance/publications/pages/publication11151en.pdf

World Alzheimer’s Report 2015: The Global Impact of Dementia, an Analysis of Prevalence, Incidence, Cost and Trends. Technical Report, Alzheimer’s Disease Internationalhttps://www.alz.co.uk/research/WorldAlzheimerReport2015.pdf

Smith, D., Lovell, J., Weller, C., Kennedy, B., Winbolt, M., Young, C., & Ibrahim, J. (2017). A systematic review of medication non-adherence in persons with dementia or cognitive impairment. PLOS ONE, 12(2), e0170651. doi:10.1371/journal.pone.0170651

From Care in Homes to Care at Home: European Experiences with (De)institutionalisation in Long-Term Care. Technical Report, European Centre for Social Welfare Policy and Researchhttps://www.euro.centre.org/downloads/detail/1540usg=AOvVaw09RDY4Um6Pz4aqzQuQxvfA

Rising Need for Elder Care in Europe Necessitates; New Paradigm for Elder Caregiving Training: A Landscape Analysis. Technical Report, European Institute of Innovation and Technologyhttps://www.kcsc.org.uk/sites/kcsc.org.uk/files/documents/Transformation/Events/CARE%20Landscape%20Analysis%20-%20EIT%20Format.pdf

Kim, S. (2015). Cognitive rehabilitation for elderly people with early-stage Alzheimer’s disease. Journal of Physical Therapy Science, 27(2), 543-546. doi:10.1589/jpts.27.543

Foster, L., & Walker, A. (2014). Active and Successful Aging: A European Policy Perspective. The Gerontologist, 55(1), 83-90. doi:10.1093/geront/gnu028

Marsillas, S., De Donder, L., Kardol, T., van Regenmortel, S., Dury, S., Brosens, D., … Varela, J. (2017). Does active ageing contribute to life satisfaction for older people? Testing a new model of active ageing. European Journal of Ageing, 14(3), 295-310. doi:10.1007/s10433-017-0413-8

Improving Later Life. Understanding the Oldest Old. Technical Report, Age UK, 2013https://www.ageuk.org.uk/globalassets/age-uk/documents/reports-and-publications/reports-and-briefings/health–wellbeing/rb_feb13_understanding_the_oldest_old_improving_later_life.pdf

Buddyhttps://buddytherobot.com

InTouch Healthhttps://www.intouchhealth.com/

Sanbot Nanohttp://en.sanbot.com

Pepperhttps://www.softbankrobotics.com/emea/en/pepper

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

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

Chang, C., Hinze, A., Bowen, J., Gilbert, L., & Starkey, N. (2018). Mymemory: A mobile memory assistant for people with traumatic brain injury. International Journal of Human-Computer Studies, 117, 4-19. doi:10.1016/j.ijhcs.2018.02.006

Nakamura, M. (2018). Virtual Care Giver: Virtual Agent for Personalized Home Elderly Care. Impact, 2018(11), 31-33. doi:10.21820/23987073.2018.11.31

Shaked, N. A. (2017). Avatars and virtual agents – relationship interfaces for the elderly. Healthcare Technology Letters, 4(3), 83-87. doi:10.1049/htl.2017.0009

Nalepa, G. J., Kutt, K., & Bobek, S. (2019). Mobile platform for affective context-aware systems. Future Generation Computer Systems, 92, 490-503. doi:10.1016/j.future.2018.02.033

Krause, A., Smailagic, A., & Siewiorek, D. P. (2006). Context-aware mobile computing: learning context- dependent personal preferences from a wearable sensor array. IEEE Transactions on Mobile Computing, 5(2), 113-127. doi:10.1109/tmc.2006.18

Kuchar, D. L., Thorburn, C. W., & Sammel, N. L. (1987). Prediction of serious arrhythmic events after myocardial infarction: Signal-averaged electrocardiogram, holter monitoring and radionuclide ventriculography. Journal of the American College of Cardiology, 9(3), 531-538. doi:10.1016/s0735-1097(87)80045-1

Veyrier, J., Maille, B., Dognin, N., Martinez, E., Tovmassian, L., Simoni, A. S., … Deharo, J. C. (2019). A real life study, analyzing clinical and economic performance of prolonged Holter Monitoring after a cryptogenic stroke. Archives of Cardiovascular Diseases Supplements, 11(1), 85. doi:10.1016/j.acvdsp.2018.10.187

empaticahttps://www.empatica.com/en-eu/

Shoval, N., Schvimer, Y., & Tamir, M. (2017). Real-Time Measurement of Tourists’ Objective and Subjective Emotions in Time and Space. Journal of Travel Research, 57(1), 3-16. doi:10.1177/0047287517691155

Electrocardiogram Standard Limb Leads (Bipolar)https://www.cvphysiology.com/Arrhythmias/A013a

AD8232https://www.analog.com/en/products/ad8232.html#

Bluetoothhttps://www.bluetooth.com/specifications/gatt/services

M5Stackhttps://m5stack.com

ESP-32https://www.espressif.com/en/products/hardware/esp32/overview

Bradley, M. M., & Lang, P. J. (1994). Measuring emotion: The self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1), 49-59. doi:10.1016/0005-7916(94)90063-9

Koelstra, S., Muhl, C., Soleymani, M., Jong-Seok Lee, Yazdani, A., Ebrahimi, T., … Patras, I. (2012). DEAP: A Database for Emotion Analysis ;Using Physiological Signals. IEEE Transactions on Affective Computing, 3(1), 18-31. doi:10.1109/t-affc.2011.15

Picard, R. W., Vyzas, E., & Healey, J. (2001). Toward machine emotional intelligence: analysis of affective physiological state. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10), 1175-1191. doi:10.1109/34.954607

Ahlstrom, M. L., & Tompkins, W. J. (1985). Digital Filters for Real-Time ECG Signal Processing Using Microprocessors. IEEE Transactions on Biomedical Engineering, BME-32(9), 708-713. doi:10.1109/tbme.1985.325589

Drake, J. D. M., & Callaghan, J. P. (2006). Elimination of electrocardiogram contamination from electromyogram signals: An evaluation of currently used removal techniques. Journal of Electromyography and Kinesiology, 16(2), 175-187. doi:10.1016/j.jelekin.2005.07.003

Nacke, L. E., Nacke, A., & Lindley, C. A. (2009). Brain Training for Silver Gamers: Effects of Age and Game Form on Effectiveness, Efficiency, Self-Assessment, and Gameplay Experience. CyberPsychology & Behavior, 12(5), 493-499. doi:10.1089/cpb.2009.0013

Ertel, K. A., Glymour, M. M., & Berkman, L. F. (2008). Effects of Social Integration on Preserving Memory Function in a Nationally Representative US Elderly Population. American Journal of Public Health, 98(7), 1215-1220. doi:10.2105/ajph.2007.113654

Costa, A., Rincon, J. A., Carrascosa, C., Julian, V., & Novais, P. (2019). Emotions detection on an ambient intelligent system using wearable devices. Future Generation Computer Systems, 92, 479-489. doi:10.1016/j.future.2018.03.038

NHS choices—Exercises for Older Peoplehttps://www.nhs.uk/Tools/Documents/NHSExercisesForOlderPeople.pdf

[-]

recommendations

 

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

Mostrar el registro completo del ítem