Sleep/wake measurement using a non-contact biomotion sensor

J Sleep Res. 2011 Jun;20(2):356-66. doi: 10.1111/j.1365-2869.2010.00876.x. Epub 2010 Aug 12.


We studied a novel non-contact biomotion sensor, which has been developed for identifying sleep/wake patterns in adult humans. The biomotion sensor uses ultra low-power reflected radiofrequency waves to determine the movement of a subject during sleep. An automated classification algorithm has been developed to recognize sleep/wake states on a 30-s epoch basis based on the measured movement signal. The sensor and software were evaluated against gold-standard polysomnography on a database of 113 subjects [94 male, 19 female, age 53±13years, apnoea-hypopnea index (AHI) 22±24] being assessed for sleep-disordered breathing at a hospital-based sleep laboratory. The overall per-subject accuracy was 78%, with a Cohen's kappa of 0.38. Lower accuracy was seen in a high AHI group (AHI >15, 63 subjects) than in a low AHI group (74.8% versus 81.3%); however, most of the change in accuracy can be explained by the lower sleep efficiency of the high AHI group. Averaged across subjects, the overall sleep sensitivity was 87.3% and the wake sensitivity was 50.1%. The automated algorithm slightly overestimated sleep efficiency (bias of +4.8%) and total sleep time (TST; bias of +19min on an average TST of 288min). We conclude that the non-contact biomotion sensor can provide a valid means of measuring sleep-wake patterns in this patient population, and also allows direct visualization of respiratory movement signals.

MeSH terms

  • Actigraphy / instrumentation*
  • Adult
  • Algorithms*
  • Diagnosis, Computer-Assisted / instrumentation*
  • Equipment Design
  • Female
  • Humans
  • Male
  • Monitoring, Ambulatory / instrumentation*
  • Polysomnography / instrumentation*
  • Sensitivity and Specificity
  • Signal Processing, Computer-Assisted / instrumentation*
  • Sleep Apnea, Obstructive / diagnosis*
  • Sleep*
  • Software
  • Wakefulness*