The Xiaomi Smart Band 9 Pro has inaccurate sleep monitoring data during sleep onset and wake-up
This Xiaomi Smart Band 9 Pro uses ACC and PPG sensors to analyze sleep stages.
For your reference, the monitoring principle is as follows:
The sleep monitoring algorithm determines sleep onset and wake-up based on changes in the accelerometer (ACC) sensor. When the ACC data shows little change for an extended period, it is considered as low activity, and the algorithm assumes you are falling asleep. Conversely, when the ACC data shows significant changes, it suggests high activity, and the algorithm assumes you are waking up. In situations such as using a phone or watching movies, the changes in ACC data are minimal, which will lead to false detection of sleep onset.
If the sleep advanced monitoring switch and the sleep breathing score switch are enabled, the algorithm will also use data from the PPG sensor. This combination of ACC and PPG data helps analyze sleep stages and generate sleep scores.
For your reference, the reasons for inaccurate sleep onset and wake-up detection are as follows:
1. If the wearable device does not register sleep or records less sleep duration than the actual sleep time, it can be due to excessive wrist movement during sleep. The sensor then will classify it as non-sleep and fail to detect or shorten the sleep duration;
2. If the wearable device records more sleep than the actual sleep time, this is because the wrist remains relatively still in non-sleep periods (e.g., using a phone before bed or sitting quietly watching a movie), and the sensor will incorrectly assume sleep onset;
3. Ensure that you are not using a double-loop leather strap in black or white. Using these straps can prevent the back of the band from making direct contact with the skin, which will interfere with sleep detection and cause inaccurate wake-up detection.
Note:
1. The algorithm is continuously optimized to reduce sleep detection errors, however, due to the limitations of the algorithm's detection principles, similar issues are common across the industry;
2. For quiet non-sleep scenarios, the use of multiple sensors and auxiliary devices is being promoted to improve detection accuracy.
For your reference, the monitoring principle is as follows:
The sleep monitoring algorithm determines sleep onset and wake-up based on changes in the accelerometer (ACC) sensor. When the ACC data shows little change for an extended period, it is considered as low activity, and the algorithm assumes you are falling asleep. Conversely, when the ACC data shows significant changes, it suggests high activity, and the algorithm assumes you are waking up. In situations such as using a phone or watching movies, the changes in ACC data are minimal, which will lead to false detection of sleep onset.
If the sleep advanced monitoring switch and the sleep breathing score switch are enabled, the algorithm will also use data from the PPG sensor. This combination of ACC and PPG data helps analyze sleep stages and generate sleep scores.
For your reference, the reasons for inaccurate sleep onset and wake-up detection are as follows:
1. If the wearable device does not register sleep or records less sleep duration than the actual sleep time, it can be due to excessive wrist movement during sleep. The sensor then will classify it as non-sleep and fail to detect or shorten the sleep duration;
2. If the wearable device records more sleep than the actual sleep time, this is because the wrist remains relatively still in non-sleep periods (e.g., using a phone before bed or sitting quietly watching a movie), and the sensor will incorrectly assume sleep onset;
3. Ensure that you are not using a double-loop leather strap in black or white. Using these straps can prevent the back of the band from making direct contact with the skin, which will interfere with sleep detection and cause inaccurate wake-up detection.
Note:
1. The algorithm is continuously optimized to reduce sleep detection errors, however, due to the limitations of the algorithm's detection principles, similar issues are common across the industry;
2. For quiet non-sleep scenarios, the use of multiple sensors and auxiliary devices is being promoted to improve detection accuracy.
Does it help?
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