基于睡眠传感器的身体恢复指数预测数据

基于睡眠传感器采集的深睡眠比例、翻身次数、入睡时间、睡眠时长数据，建立身体恢复指数预测模型，用于评价用户睡眠期间的身体恢复程度。此数据主要应用于用户的日常健康管理服务，将身体恢复程度按从低到高划分5个等级，等级越高，表示身体恢复程度越好。1）用户可根据此数据调整日常作息时间，使得身体恢复更为理想。2）用户可基于此数据评估运动后的身体恢复情况，有助于安排下一次运动计划。1、 将批量的深睡眠比例、翻身次数、入睡时间、睡眠时长数据进行无量纲标准化。
2、 将标准化后的数据样本输入到线性回归模型中，进行训练学习，样本标签来自用户起床后的主观身体恢复感受。
3、 模型预测结果分布在[0,1]之间，越接近1，表示身体恢复程度越好，反之越接近0，表示身体恢复程度越差。
4、 按一定的阈值将[0,1]区间划分为5个等级区间，根据模型预测结果所在的等级区间，确定身体恢复指数。模型结果[0-0.2]、(0.2-0.4]、(0.4-0.6]、(0.6-0.8]、(0.8-1.0]分别对应身体恢复指数1、2、3、4、5，指数越大，表示身体恢复程度越好。

Based on data including deep sleep proportion, number of sleep turns, sleep onset time and total sleep duration collected by sleep sensors, a physical recovery index prediction model is established to assess users' physical recovery status during sleep. This dataset is primarily utilized for users' daily health management services. The physical recovery degree is categorized into 5 levels from low to high, where a higher level denotes better physical recovery. 1) Users can adjust their daily routine based on this data to optimize physical recovery. 2) Users can evaluate their post-exercise physical recovery status using this data, which assists in formulating subsequent exercise plans. 1. Perform dimensionless standardization on batch datasets including deep sleep proportion, number of sleep turns, sleep onset time and total sleep duration. 2. Input the standardized data samples into a linear regression model for training, with sample labels derived from users' subjective perception of physical recovery upon waking up. 3. The model's prediction outputs range within the interval [0, 1]. The closer the output value is to 1, the better the physical recovery degree; conversely, the closer the value is to 0, the poorer the physical recovery degree. 4. Divide the [0, 1] interval into 5 grade intervals with preset thresholds, and determine the physical recovery index according to the interval where the model's prediction result falls. The model results [0-0.2], (0.2-0.4], (0.4-0.6], (0.6-0.8], (0.8-1.0] correspond to physical recovery indices 1, 2, 3, 4, 5 respectively, with larger indices indicating better physical recovery.

搜集汇总

数据集介绍

特点

该数据集包含基于睡眠传感器的身体恢复指数预测数据，共1001条记录，每日更新。数据用于建立身体恢复指数预测模型，帮助用户评估睡眠质量和身体恢复程度，适用于健康管理服务。