Skip to main content

Data Collection and Analysis Tools: From the Home to the Cloud

  • Chapter
  • First Online:
Supportive Smart Homes

Abstract

In the previous chapter, we introduced a variety of sensors capable of generating a number of data types, however, their data only become useful once they are analyzed and interpreted. In the supportive smart home, this analysis and interpretation is what will initiate actions. For instance, if we assume a digital light camera is like an eye, it will ‘see’ whatever is within its range, but by itself, it is unable to accomplish much more than that. Yet, just like our eyes need to be connected to our brain for us to interpret and potentially act, the video camera also needs to be connected to a computer (or have a person literally watching what the camera is seeing) for any action to occur. To be really concrete, a human eyeball may ‘see’ a lion, but the eyeball would likely get eaten unless it were attached to a person’s—capable of analyzing and interpreting what the eyeball is seeing—and identifying it as a physical threat. The brain would then hopefully cue said person to take some fast, defensive action away from the lion.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Almhairat S et al (2021) Supportive smart home systems: utilization assessment for internet service provider networks. In: 2021 IEEE international symposium on medical measurements and applications (MeMeA). IEEE, pp 1–6. https://doi.org/10.1109/MeMeA52024.2021.9478744

  • Ault L et al (2020) Smart home technology solution for night-time wandering in persons with dementia. J Rehab Assist Technol Eng 2020(7). https://doi.org/10.1177/2055668320938591

  • Azimi H et al (2020) Cloud processing of bed pressure sensor data to detect sleep apnea events. In: 2020 IEEE international symposium on medical measurements and applications (MeMeA). IEEE, pp 1–5. https://doi.org/10.1109/MeMeA49120.2020.9137203

  • Casaccia S et al (2020) Measurement of users’ well-being through domotic sensors and machine learning algorithms. IEEE Sens J 20(14):8029–8038. https://doi.org/10.1109/JSEN.2020.2981209

    Article  Google Scholar 

  • Gilakjani S et al (2018) Long-term sleep assessment by unobtrusive pressure sensor arrays. In: ICBSP 2018: proceedings of the 2018 3rd international conference on biomedical imaging, signal processing. ICBSP, pp 23–35. https://doi.org/10.1145/3288200.3288214

  • Knoefel et al (2019) Cloud based artificial intelligence processing of ambient home sensors. In: ISPIM conference proceedings. The international society for professional innovation management (ISPIM) . Manchester, pp 1–12

    Google Scholar 

  • Monteriù A et al (2018) A smart sensing architecture for domestic monitoring: methodological approach and experimental validation. Sensors 18(7):2310. https://doi.org/10.3390/s18072310

    Article  Google Scholar 

  • Morresi N et al (2022) Heterogeneous sensor network for the measurement of dementia progression and well-being: preliminary study. In: 2022 IEEE international symposium on medical measurements and applications (MeMeA). IEEE, pp 1–6. https://doi.org/10.1109/MeMeA54994.2022.9856557

  • Rajkumar RP (2020) COVID-19 and mental health: a review of the existing literature. Asian J Psychiatr 52:102066. https://doi.org/10.1016/j.ajp.2020.102066

    Article  Google Scholar 

  • Wallace et al (2019) Cloud based artificial intelligence processing of ambient home sensors. In: ISPIM conference proceedings. The international society for professional innovation management (ISPIM) . Manchester, pp 1–12

    Google Scholar 

  • Wallace B et al (2020) Comparison of bed-sensors for nocturnal behaviour assessment. In: eTELEMED 2020: the twelfth international conference on ehealth, telemedicine, and social medicine. https://www.thinkmind.org/articles/etelemed_2020_3_220_40094.pdf. Accessed 12 Mar 2023

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Knoefel, F., Wallace, B., Thomas, N., Sveistrup, H., Goubran, R., Laurin, C.L. (2024). Data Collection and Analysis Tools: From the Home to the Cloud. In: Supportive Smart Homes. Synthesis Lectures on Technology and Health. Springer, Cham. https://doi.org/10.1007/978-3-031-37337-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-37337-4_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37336-7

  • Online ISBN: 978-3-031-37337-4

  • eBook Packages: Synthesis Collection of Technology (R0)

Publish with us

Policies and ethics