基于睡眠传感器的浅睡时长数据

基于睡眠传感器采集的夜间睡眠期间用户心率、呼吸信号数据，通过算法计算出睡眠中的浅睡部分。1）用户可根据此数据，知晓当晚睡眠中浅睡时长、浅睡占比。2）用户可以根据历史浅睡时长数据的变化趋势，了解自己的睡眠周期，并依据此做出调整。3）通过长期的数据监测，可以了解季节、健康状况等对用户睡眠的影响，在不同的阶段给予用户更具指导性的健康建议。1. 将传感器收集到的心跳、呼吸信号片段进行数据预处理；
2. 对预处理好的的心跳、呼吸信号进行心率、呼吸率指标计算；
3. 将各统计量放入训练好的分类模型，判断是否为浅睡，1为浅睡，0为非浅睡；
4. 浅睡时长计算，对是否为浅睡字段中为1的片段进行累加，获得整晚的浅睡时长。

This dataset leverages heart rate and respiratory signal data collected by sleep sensors from users during nighttime sleep, and utilizes algorithms to identify light sleep segments during sleep. 1) Users can acquire their total light sleep duration and light sleep proportion for the current night using this dataset. 2) Users can recognize their personal sleep cycles based on the variation trends of historical light sleep duration data, and make corresponding adjustments. 3) Through long-term data monitoring, the effects of factors including seasons and health conditions on users' sleep can be evaluated, and more targeted health guidance can be offered to users at different stages.

The specific processing workflow is as follows:
1. Perform data preprocessing on the heartbeat and respiratory signal segments collected by the sensors;
2. Compute heart rate and respiratory rate indicators from the preprocessed heartbeat and respiratory signals;
3. Feed the calculated statistical metrics into the trained classification model to judge whether a segment belongs to light sleep, with 1 indicating light sleep and 0 indicating non-light sleep;
4. Calculate the total light sleep duration by summing up all segments labeled as 1 in the light sleep judgment field, thereby obtaining the total light sleep duration of the entire night.