基于个人体质的运动推荐指数数据

通过综合分析个人的基础数据，以及对用户体质的诊断分析，利用专有算法计算出个性化的运动推荐指数，为用户提供科学的运动建议。
根据该指数了解用户的运动需求，根据用户的体质状况推荐合适的日常运动。

本数据适用于：
养老机构：帮助老年人更好地理解和管理自己的身体体质，提供更加安全的活动指南。
健身中心与体育俱乐部：为会员提供科学的运动指导，根据客户的身体体质制定训练计划，确保运动的安全性。通过本公司中医诊断产品，完成对用户体质的评测；将数据预处理后，输入到随机森林模型中，得出运动推荐指数。
随机森林模型是通过构建多棵决策树并综合其预测结果。每棵决策树会根据身体指标特征重要性和分裂规则，对数据进行逐步的节点分裂，直至达到叶节点。
1.节点分裂：在每个内部节点，决策树根据某个身体指标特征向量及其阈值对数据进行分裂，选择信息增益最大的身体指标特征点和阈值。计算公式为：
Information Gain = H(S) - Σ (v ∈ Values(A)) |S_v| / |S| * H(S_v)
其中，H(S)是样本集 S 的熵，A 是特征，是特征 A 取值为时的样本子集。
2.综合多棵决策树的预测结果，最终的运动推荐指数公式为：
(1/T) * ∑(从 t=1 到 T) y_t^(X)
其中，T是决策树的数量，y是第t棵决策树的预测值。X是输入的身体指标特征向量（身高、体重、年龄、劳动强度、心率、中医体质）。

By comprehensively analyzing an individual's basic data and physical constitution diagnosis results, a proprietary algorithm is used to calculate a personalized exercise recommendation index, providing users with evidence-based exercise advice. Based on this index, the user's exercise needs are identified, and appropriate daily exercises are recommended according to the user's physical constitution.

This dataset is applicable to:
1. Nursing homes: Helping the elderly better understand and manage their physical conditions, and providing safer activity guidelines.
2. Fitness centers and sports clubs: Providing scientific exercise guidance for members, formulating training plans based on clients' physical constitutions to ensure exercise safety.
The physical constitution assessment of users is completed through the company's traditional Chinese medicine (TCM) diagnosis products. After data preprocessing, the processed data is input into a random forest model to derive the exercise recommendation index.

The random forest model constructs multiple decision trees and integrates their prediction results. Each decision tree performs gradual node splitting on the data based on the importance of physical indicator features and splitting rules, until reaching leaf nodes.
1. Node splitting: At each internal node, the decision tree splits the data according to a certain physical indicator feature vector and its threshold, selecting the physical indicator feature point and threshold with the maximum information gain. The calculation formula is:
Information Gain = H(S) - Σ (v ∈ Values(A)) |S_v| / |S| * H(S_v)
Where H(S) is the entropy of the sample set S, A is the feature, and the sample subset when feature A takes the value v.
2. To integrate the prediction results of multiple decision trees, the final exercise recommendation index formula is:
(1/T) * ∑(from t=1 to T) y_t(X)
Where T is the number of decision trees, y_t is the predicted value of the t-th decision tree, and X is the input physical indicator feature vector (height, weight, age, labor intensity, heart rate, TCM constitution).

搜集汇总

数据集介绍

特点

该数据集包含1001条个人体质数据，每日更新，通过随机森林模型计算运动推荐指数，适用于养老机构和健身中心等场景，为用户提供科学的运动建议。