Abstract
This paper presents the initial data analysis and modelling for detecting health changes from data gathered on a low-cost smartphone used during normal daily activities. The work is part of the ENVELLINT project, where one of the main objectives is to explore if it is possible to evaluate the functional aspects of frailty indices automatically using smartphones.
The project involves both longitudinal and cross-sectional studies involving elderly participants. In the longitudinal study a comprehensive set of sensor, application and other smartphone data is gathered over lengthy periods for each participant, together with extensive medical assessments. The purpose is to provide a comprehensive data set for investigating frailty and health changes. The larger cross-sectional study, which included only the medical assessments, was necessary to gather more medical related health and frailty data, and to balance project costs.
The analysis work to date has involved data and feature engineering to identify, extract and select the most useful features. Insights are given for the potential use of the location and application usage features.
A core aspect, given the expense and the limited number of participants in the longitudinal study, is to explore the use of synthetic data generation to leverage the real data from both studies. Generative Adversarial Network and Gaussian Copula models have been investigated to create a larger representative dataset of longitudinal participants. Initial results and insights show generated synthetic data that closely mirrors the real data, especially using Gaussian Copula.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
OECD: Health at a Glance 2021 (Ageing and Long Term Care: Demographic trends): OECD Indicators, OECD Publishing, Paris (2021), https://doi.org/10.1787/ae3016b9-en
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Xu, L., Skoularidou, M., Cuesta-Infante, A., Veeramachaneni, K.: Modeling tabular data using conditional GAN. In: Proceedings of the 33rd International Conference on Neural In-formation Processing Systems, Article 659, pp. 7335–7345. Curran Associates Inc., Red Hook, NY, USA (2019)
Kamthe, S., Assefa, S., Deisenroth, M.: Copula flows for synthetic data generation. arXiv preprint arXiv:2101.00598 (2021)
Rodríguez-Martín, D., et al.: A waist-worn inertial measurement unit for long-term monitoring of Parkinson’s disease patients. Sensors 17(4), 827 (2017)
Rodríguez-Martín, D., Samà, A., Pérez-López, C., et al.: Posture transition analysis with barometers: contribution to accelerometer-based algorithms. Neural Comput. Appl. 32, 335–349 (2020). https://doi.org/10.1007/s00521-018-3759-8
Pérez-López, C., et al.: Dopaminergic-induced dyskinesia assessment based on a single belt-worn accelerometer. Artif. Intell. Med. 67, 47–56 (2016). https://doi.org/10.1016/j.artmed.2016.01.001
Fried, L.P., et al.: Frailty in older adults: evidence for a phenotype. J. Gerontol Ser. A Biol. Sci. Med. Sci. 56, M146–M157 (2001). https://doi.org/10.1093/gerona/56.3.M146
Amblàs-Novellas, J., Martori, J.C., Brunet, N.M., Oller, R., Gómez-Batiste, X., Panicot, J.E.: Revista Espa-ñola de Geriatría y Gerontología Índice frágil-VIG: dis no y evaluación de un índice de fragilidad basado en la Valoración Integral Geriátrica, Rev Esp Geriatr Gerontol. (2017). https://doi.org/10.1016/j.regg.2016.09.003
Lobo, A., Ezquerra, J., Gómez Burgada, F., Sala, J.M., Seva Díaz, A.: El Mini-Examen Cog-noscitivo (un test sencillo, práctico, para detectar alteraciones intelectuales en pacientes médi-cos)., Actas Luso. Esp. Neurol. Psiquiatr. Cienc. Afines. (1979)
Mahoney, F., Barthel, D.: Functional evaluation: the Barthel index. Md State Med J. 14, 61–65 (1965)
Rockwood, K., Rockwood, M.R.H., Mitnitski, A.: Physiological redundancy in older adults in relation to the change with age in the slope of a frailty index. J. Am. Geriatr. Soc. 58, 318–323 (2010). https://doi.org/10.1111/j.1532-5415.2009.02667.x
Lawton, M.P., Brody, E.M.: Assessment of older people: self-maintaining and instrumental V1.1 – 24/12/2019 20 activities of daily living. Gerontologist 9, 179–86 (1969). http://www.ncbi.nlm.nih.gov/pubmed/5349366
Martínez de la Iglesia, J., Onís Vilches, M.C., Dueñas Herrero, R., Aguado Taberné, C., Albert Colomer, C., Arias Blanco, M.C.: Abreviar lo breve.aproximación a versiones ultracortas del cuestionario de Yesavage para el cribado de la depresión. Atención Primaria 35(1), 14–21 (2005). https://doi.org/10.1157/13071040
Soler, P.A., Mellinas, G.P., Sánchez, E.M., Jiménez, E.L.: Evaluación de la comorbilidad en la población anciana: utilidad y validez de los instrumentos de medida. Rev. Esp. Geriatr. Gerontol. 45(4), 219–228 (2010). https://doi.org/10.1016/j.regg.2009.10.009
Pérez, E., et al.: Frailty Level Prediction in Older Age Using Hand Grip Strength Functions Over Time. In: Rojas, I., Joya, G., Català, A. (eds.) Advances in Computational Intelligence: 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part II, pp. 356–366. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-85099-9_29
Synthetic Data Metrics. Version 0.9.3. DataCebo, Inc. (2023). https://docs.sdv.dev/sdmetrics/
Acknowledgements
This work was partially supported by the Catalonia FEDER program, resolution GAH/815/2018 under the project, PECT Garraf : Envelliment actiu i saludable i dependència.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nelson, J. et al. (2023). Data Analysis and Generation in the ENVELLINT Longitudinal Study to Determine Loss of Functionality in Elderly People. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14135. Springer, Cham. https://doi.org/10.1007/978-3-031-43078-7_32
Download citation
DOI: https://doi.org/10.1007/978-3-031-43078-7_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43077-0
Online ISBN: 978-3-031-43078-7
eBook Packages: Computer ScienceComputer Science (R0)