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Realistic and Unobtrusive Solutions for Independent Ageing

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RADIO--Robots in Assisted Living

Abstract

Robotic, smart home and other related technologies that are integrated in AAL environments should be combined and presented to their users in an unobtrusive way. Obtrusiveness is a subjective, multidimensional, environment-dependent and multi-target concept that is used to describe how various technologies can negatively impact the daily lives of their users. Many of the negative implications come forward as a conflict between primary users (elderly people) and secondary users (clinical staff, (in)formal caregivers). In this chapter, we present the methodology followed during the design and development of the RADIO system to guarantee that the final solution is the least obtrusive for its users. To begin with, primary and secondary user requirements are set under the prism of obtrusiveness dimensions and possible conflicts are described. The major clash presented is the gap between individual perceptions on privacy and, reliable and sound clinical information. Bridging this gap depends most of the time on the means (sensors) used to monitor clinically interesting activities and the state of the art of the technical methods that exploit these data. The level of obtrusiveness of using a specific sensor depends on what activity is monitored as well as where this monitoring happens. In the processing pipeline that follows raw data collection, the main consideration is the type of output information (raw data, activity log, aggregates on logs). To put all this information together, we introduce a decision-making process for including a clinical assessment item in a monitoring system and we demonstrate with examples how this workflow can be followed in developing system’s such as RADIO. Finally, we discuss several actions that must be taken in the design and development of system such as RADIO to ensure that overall unobtrusiveness is satisfied. Assigning obtrusiveness implications to a certain clinical assessment item highly depends on subjectivity of several constructs. However, here we define a framework within which intrusiveness constructs should be discussed and negotiated.

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Notes

  1. 1.

    RADIO system that is targeted to primary and secondary users. Moreover, an Ambient Assistive Living (AAL) environment can also impact people that do not live in it but spend time in it (e.g. visitors of the primary users, see Sect. 3.10 of Chap. 3).

  2. 2.

    In the RADIO system, InterRAI Long-Term Care Facilities and Home Care instruments were considered for selecting clinical requirementshttp://www.interrai.org/instruments/.

References

  1. Hensel, B. K., Demiris, G., & Courtney, K. L. (2006). Defining obtrusiveness in home telehealth technologies: A conceptual framework. Journal of the American Medical Informatics Association, 13(4), 428–431.

    Article  Google Scholar 

  2. Koehler, M., Rabinowitz, T., Hirdes, J., Stones, M., Carpenter, G. I., Fries, B. E., et al. (2005). Measuring depression in nursing home residents with the MDS and GDS: An observational psychometric study. BMC Geriatrics, 5, 1. Epub 2005/01/04.

    Article  Google Scholar 

  3. Martinez de la, I. J., Onís-Vilches, M., Dueñas-herrero, R., Taberné-Aguado, C., Albert-Colomer, C. (2005). Abreviar lo breve. Aproximación a versiones ultracortas del cuestionario de Yesavage para el cribado de la depresión. Aten Primaria, 35(1).

    Google Scholar 

  4. Rinaldi, P., Mecocci, P., Benedetti, C., Ercolani, S., Bregnocchi, M., Menculini, G., et al. (2003). Validation of the five-item geriatric depression scale in elderly subjects in three different settings. Journal of the American Geriatrics Society, 51(5), 694–698.

    Article  Google Scholar 

  5. Alonso-Martin, F., Malfaz, M., Sequeira, J., Gorostiza, J. F., & Salichs, M. A. (2013). A multimodal emotion detection system during human-robot interaction. Sensors, 13(11), 15549–15581.

    Article  Google Scholar 

  6. Wang, K. C. (2015). Time-frequency feature representation using multi-resolution texture analysis and acoustic activity detector for real-life speech emotion recognition. Sensors, 15(1), 1458–1478.

    Article  Google Scholar 

  7. Mano, L. Y., et al. (2016). Exploiting IoT technologies for enhancing health smart homes through patient identification and emotion recognition. Computer Communications, 89, 178–190.

    Google Scholar 

  8. Mao, Q., et al. (2014). Learning salient features for speech emotion recognition using convolutional neural networks. IEEE Transactions on Multimedia, 16(8), 2203–2213.

    Google Scholar 

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Correspondence to M. Dagioglou .

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Dagioglou, M., Ariño Blasco, S., Navarro Llobet, D., Konstantopoulos, S. (2019). Realistic and Unobtrusive Solutions for Independent Ageing. In: Karkaletsis, V., Konstantopoulos, S., Voros, N., Annicchiarico, R., Dagioglou, M., Antonopoulos, C. (eds) RADIO--Robots in Assisted Living. Springer, Cham. https://doi.org/10.1007/978-3-319-92330-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-92330-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92329-1

  • Online ISBN: 978-3-319-92330-7

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