Smart Watch

Technology that combines Smart Watch Data to help with Sound Sleep

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New smart watch algorithms can help identify why you are sleeping poorly

Smart watches have come a long way in recent years and can be made to do fascinating things. Equipped with algorithms that have potential to collect rich data from these wearable, these data can be utilized to obtain useful, practical recommendations. One such application can be identifying healthy sleep patterns in wearers. Enabled by algorithms, smart watches can double as non-medical grade sleep monitors. A team of eight researchers from Northwest University, China and Lancaster University, UK have developed a software which they call SleepGuard that utilizes several non-biomedical data from multiple smart watches to monitor sleep patterns of the wearer, thereby helping them get sound sleep. The monitoring technology thus developed is better than many currently available consumer or non-medical grade sleep monitors.

Technology developed pools Data from Multiple Sensors in Smartphones to detect Healthy Sleep Patterns

The technology combines data from multiple smart watch sensors. These data pertain to a wide range of non-biomedical parameters such as ambient light, breathing patterns, sleeping postures, bedtime routines, and numerous other health-related factors related to sleeping environment. The factors behind sound sleep are multifaceted and a better understanding of the whole mechanism must take these into account.

The study revealed that capturing numerous characteristics of body movements is key to identifying healthy sleep patterns. The presence of several sensors in smart watches such as gyroscope, accelerometer, orientation sensor, helped researchers to detect different hand and body movements of the wearers during sleep. These data were utilized by algorithms to identify problematic sleep postures and the role of ambient light. For instance, the technology could identify three common hand positions of the wearer, which can disrupt normal sleep. The algorithm could also count the number of rollovers in sleep and posited that too many of them interfere with normal sleeping pattern.

Algorithms track only Non-biomedical Factors 

The researchers contended that the technology can only capture physical and non-biomedical factors related to sleep, rather than tracking biomedical signals. So it lacks the accuracy of medical-grade ones. Nonetheless, they are affordable to people at large and offers a user-friendly operation, especially for home users.

The work is detailed in an article published ACM Digital Library on September 3, 2018.

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