This product uses pressure sensors to monitor the heart rate, breathing rate, movement, in/out of bed, awake/asleep, etc. of the elderly in real time. The data is uploaded to the cloud system in real time, and if an abnormal situation occurs, an alarm is quickly triggered, providing the elderly with timely help/rescue. Generates a sleep quality report and provides sleep habits, sleep status, and other report analysis. The connection method is WiFi.
Based on the accurate monitoring and non-contact features of the body monitoring mattress, it can be widely used in various applications that require large-scale monitoring of in-bed conditions, such as nighttime sleep analysis for teenagers, continuous health monitoring, early warning and screening for corporate employees, centralized care monitoring in nursing homes, remote health care for the elderly, and continuous sub-health status monitoring for ordinary adults. The monitoring mattress meets various non-contact physiological monitoring needs.
? The intelligent monitoring mattress adopts its own detection technology based on piezoelectric (capacitive) sensing, and can achieve high-precision detection of human physiological signs and movements without contact: heartbeat, breathing, movement, in bed/out of bed, etc.
? Using the accompanying precise algorithms, it can accurately detect many data from the human body in real time as needed, such as heart rate, breathing rate, and so on, and assist various application systems to calculate data such as sleep habits.
? The intelligent monitoring mattress can output monitoring results in real time through WiFi according to different application scenarios. Supports socket-based connection methods, connecting with local network or Internet cloud servers.
Function | Monitoring Range/Specification | Description | Remark |
Heart Rate Accuracy | Detection range: under normal vital signs, 50 ~ 120 times/m, deviation range ±5 times/m, whichever is greater; Compared with medical monitor, the time when the deviation is within ±5 should not be less than 90% per day. | The comparison scenario is the subject lying in a supine, resting state |
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Breathing Rate Accuracy | Detection range: under normal vital signs, 10 ~ 30 times/m, deviation range ±4 times/m, whichever is greater; Compared with medical monitor, the time when the deviation is within ±2 should not be less than 90% per day. | The comparison scenario is the subject lying in a supine, resting state |
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Heart Rate | 50～120 times/m | Output stable values within 20 seconds after the body is still, and update the values per second within the previous 30 seconds every 5 seconds |
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Breathing Rate | 10～30 times/m | Output stable values within 30 seconds after the body is still, and update the values per second within the previous 30 seconds every 5 seconds |
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In/out of bed | Support | Fast mode responds within 10 seconds, delay mode responds within 20 seconds |
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Sleep analysis | Provide sleep analysis and scoring rules | Implemented on the server based on the rules | Need to be supported by the application platform |
Body motion detection | Support | responds within 5 seconds |
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