Under the Sheets: A New Noninvasive Sleep-Monitoring System

Approximately a third of the population suffers from difficulty falling asleep, frequent waking, poor quality of sleep, and a variety of sleep-related breathing problems. For older adults, there are concerns that medical emergencies that occur during sleep could go unnoticed. Unfortunately, most current devices for monitoring sleep are uncomfortable and used primarily for making medical diagnoses. However, many health benefits could result from an unobtrusive way to monitor sleep in a home environment.

Recently, a PhD dissertation at the University of Helsinki examined the effectiveness of a sleep-monitoring system intended to be used in the home. This system involves a sensor placed under bed sheets that can measure the sleeper’s heart rate, breathing, movement, sleep quality, and sleep length. These can all be measured without the sensor coming into direct contact with the body. Data from this sensor can then be transmitted to a mobile phone application or to an online service, so that the quality and quantity of sleep can be followed by the subject or a health professional. This system also permits sleepers to determine how their daily habits and lifestyle choices may be affecting their sleep.

Although the most accurate measurement of sleep patterns requires worn sensors, this system proved able to detect 82 percent of the respiratory cycles of the sleepers measured. For heart rate, the accuracy can differ from person to person, but the researcher writes that, “The average error in resting heart rate is less than 1 BPM [beat per minute] with most subjects, which enables sufficiently precise average HR [heart rate] measurement for many applications.” For some sleepers, the system is precise enough to also assess heart rate variability. If two sleepers share a bed, the sensor will still work, as long as each sleeper does not change sides of the bed.

The main feature of the device’s reporting is what is called a hypogram, which shows the amounts of wakefulness, REM sleep, light sleep, and deep sleep. Restlessness is also shown by plotting how much movement a sleeper has in different parts of the night. The user also gets heart rate information in the form of a trend curve, as well as resting heart rate and stress reactions, which are based on heart rate data. In addition, the user gets data on room temperature, brightness, and noise levels in order to assess potential problems in the sleeping environment. At present, emergency notification of cardiac or respiratory activity that could be a sign of a life-threatening medical emergency is not built into this sleep monitoring system, but given the data collected by the system, such a feature appears feasible. The creators of the sleep monitoring system will be making an interface available that will allow programmers to access generated information in machine-understandable form. This will permit the creation of additional mobile, desktop, or web applications that use the sleep data obtained by this device.

This unobtrusive sleep measurement system will be commercially available in the United States and Europe as a product named Beddit at some point in 2014.


Paalasmaa J. Monitoring Sleep with Force Sensor Measurement [dissertation]. Helsinki, Finland: University of Helsinki; 2014.


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