In the previous two posts, we reverse-engineered the Actiwatch 2 algorithm for deriving sleep/wake patterns and sleep parameters from raw acceleration (ACC) data. This post applied the same algorithms to 10 nights of raw-ACC data obtained from Vivosmart 4 and compared the results with Actiwatch 2.
As described here, we found both the sleep/wake patterns and sleep parameters derived from Vivosmart 4 to be consistent with that of Actiwatch 2. The result suggests that an affordable, consumer wearable such as Vivosmart 4 can reproduce the results from a more expensive, medical-grade actigraphic device like Actiwatch 2.
1. Comparison of Sleep/Wake Detection and Sleep Parameters
Figure 1 illustrates the sleep/wake patterns of the 10 nights derived from Actiwatch 2 and Vivosmart 4 (the method was introduced in a previous post). The two vertical dashed lines in each subplot indicated the Actiwatch 2 scored sleep onset and offset. Table 1 lists the consistent and inconsistent results between Actiwatch 2 and Vivosmart 4. The number in each cell is the total minutes within sleep onset and offset in the 10 nights. Treating the result from Actiwatch 2 as a standard, the sensitivity (to detect sleep), specificity, and accuracy of Vivosmart 4 are near 1.00, 0.84, and 0.99, respectively.
Table 2 compares the differences in sleep parameters (introduced here) between Actiwatch 2 and Vivosmart 4. The two watches showed good consistency as the mean differences of most of the parameters is smaller than 5 mins.
We reverse-engineered the full algorithm utilized in the Actiwatch 2 to derive nighttime sleep/wake patterns and sleep parameters from the raw acceleration signal. Both the sleep/wake patterns and sleep parameters derived from Vivosmart 4 are consistent with that of Actiwatch 2. The result suggests that an affordable, consumer wearable such as Vivosmart 4 can reproduce the results from a more expensive, medical-grade actigraphic device.
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 CamNtech, “The MotionWatch 8 and MotionWare User Guide,” Available at http://www.medicoimpianti.it/files/The- MotionWatch-User-Guide.pdf, 2018.
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Francis is a research Lead at Labfront, responsible for data validation and analysis. He is interested in applying physics or math to medical research.
Research Lead (and Designated Plant Caretaker) at Labfront. Han-Ping is a researcher who seeks interesting phenomena, especially the interdisciplinary ones. His dream is to make all the riveting research be easily explored.
Dr. Ahn is an internal medicine physician with a background in physics/engineering and physiological signal analyses. He is the Chief Medical Officer at Labfront and an Assistant Professor in Medicine & Radiology at Harvard Medical School. Dr. Ahn is passionate about democratizing health sciences and exploring health from an anti-disciplinary perspective.