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Updated: May 11, 2026

Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools
16:05

Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools

Published on: October 1, 2007

Sleep estimates using microelectromechanical systems (MEMS).

Bart H W te Lindert1, Eus J W Van Someren

  • 1Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands. b.te.lindert@nin.knaw.nl

Sleep
|May 2, 2013
PubMed
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This summary is machine-generated.

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This study developed a method to convert microelectromechanical systems (MEMS) accelerometry data into traditional actigraphy movement counts. This ensures backward compatibility for ongoing sleep studies transitioning to MEMS technology.

Area of Science:

  • Biomedical Engineering
  • Sleep Science
  • Wearable Technology

Background:

  • Actigraphy offers affordable sleep estimates but has limitations like brand-specific data reduction, hindering large-scale data pooling.
  • Microelectromechanical systems (MEMS) accelerometry provides high-rate, three-axial linear data, potentially improving sleep estimate reliability with advanced analyses.
  • A transition from actigraphy to MEMS accelerometry is occurring, necessitating methods for backward compatibility.

Purpose of the Study:

  • To design and validate a method for transforming MEMS accelerometry data into traditional actigraphic movement counts.
  • To ensure backward compatibility for ongoing studies switching from actigraphy to MEMS accelerometry.
  • To enable the use of validated sleep estimation algorithms with new MEMS data.

Main Methods:

Keywords:
accelerometryactigraphycohort studiessleep

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  • Simultaneous recording of actigraphy and MEMS accelerometry in 15 healthy adults under home, unrestrained conditions.
  • Utilized Passing-Bablok regression to transform MEMS accelerometry signals into actigraphic movement counts.
  • Employed Kappa statistics and Bland-Altman plots to assess agreement and reliability of sleep parameter estimations.

Main Results:

  • The transformation algorithm demonstrated almost perfect agreement between epochs scored as wake or sleep (kappa ≈ 0.83-0.87).
  • Sleep parameter agreement was superior when using MEMS accelerometers compared to using only actigraphs.
  • The MEMS-accelerometer and actigraph combination showed better agreement than two actigraphs.

Conclusions:

  • The developed algorithm facilitates continuity of outcome parameters for studies transitioning to MEMS accelerometers.
  • Implementation of this method ensures backward compatibility while allowing for the collection of raw MEMS data.
  • This approach supports future advanced sleep analyses and promotes cross-study data pooling.