基于睡眠传感器的深睡时长数据

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深睡部分。1）用户可根据此数据，知晓当晚睡眠中深睡时长、深睡占比。2）用户可以根据历史深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. 将各指标放入训练好的分类模型，对每2分钟的片段进行判断是否为深睡，1为深睡，0为非深睡；
4. 深睡时长计算，对是否为深睡字段中为1的片段进行累加，获得整晚的深睡时长。

This dataset is built upon user heart rate and respiratory signal data collected by sleep sensors during nighttime sleep, with deep sleep segments during sleep calculated via algorithms. 1) Users can obtain their total deep sleep duration and deep sleep proportion of that night's sleep using this data. 2) Users can grasp their personal sleep cycles based on the changing trends of historical deep sleep duration data, and make corresponding adjustments. 3) Through long-term data monitoring, the impacts of factors such as seasons and health conditions on user sleep can be analyzed, and more targeted health suggestions can be provided to users at different stages. 1. Perform data preprocessing on the heartbeat and respiratory signal segments collected by the sensors. 2. Calculate heart rate and respiratory rate indicators from the preprocessed heartbeat and respiratory signals. 3. Input each indicator into a pre-trained classification model to determine whether each 2-minute segment is a deep sleep segment, where 1 denotes deep sleep and 0 denotes non-deep sleep. 4. Calculate the total deep sleep duration by summing up all segments marked as 1 in the deep sleep judgment field, to get the overall deep sleep duration for the entire night.

搜集汇总

数据集介绍

特点

该数据集包含基于睡眠传感器采集的夜间睡眠数据，涵盖用户ID、夜间心率、夜间呼吸率、深睡状态和深睡时长等字段，每日更新，用于分析用户睡眠质量和提供健康建议。