Sleep estimates using microelectromechanical systems (MEMS)
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Updated: May 11, 2026
Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools
Published on: October 1, 2007
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
Study Objectives:
Although currently more affordable than polysomnography, actigraphic sleep estimates have disadvantages. Brand-specific differences in data reduction impede pooling of data in large-scale cohorts and may not fully exploit movement information. Sleep estimate reliability might improve by advanced analyses of three-axial, linear accelerometry data sampled at a high rate, which is now feasible using microelectromechanical systems (MEMS). However, it might take some time before these analyses become available. To provide ongoing studies with backward compatibility while already switching from actigraphy to MEMS accelerometry, we designed and validated a method to transform accelerometry data into the traditional actigraphic movement counts, thus allowing for the use of validated algorithms to estimate sleep parameters.
Design:
Simultaneous actigraphy and MEMS-accelerometry recording.
Setting:
Home, unrestrained.
Participants:
Fifteen healthy adults (23-36 y, 10 males, 5 females).
Interventions:
None.
Measurements:
Actigraphic movement counts/15-sec and 50-Hz digitized MEMS-accelerometry.
Analyses:
Passing-Bablok regression optimized transformation of MEMS-accelerometry signals to movement counts. Kappa statistics calculated agreement between individual epochs scored as wake or sleep. Bland-Altman plots evaluated reliability of common sleep variables both between and within actigraphs and MEMS-accelerometers.
Results:
Agreement between epochs was almost perfect at the low, medium, and high threshold (kappa = 0.87 ± 0.05, 0.85 ± 0.06, and 0.83 ± 0.07). Sleep parameter agreement was better between two MEMS-accelerometers or a MEMS-accelerometer and an actigraph than between two actigraphs.
Conclusions:
The algorithm allows for continuity of outcome parameters in ongoing actigraphy studies that consider switching to MEMS-accelerometers. Its implementation makes backward compatibility feasible, while collecting raw data that, in time, could provide better sleep estimates and promote cross-study data pooling.
