Mobile phones and other electronic gadgets or devices have aided in collecting data without the need for data entry. This paper will specifically focus on Mobile health data. Mobile health data use mobile devices to gather clinical health data and track patient vitals in real-time. Our study is aimed to give decisions for small or big sports teams on whether one athlete good fit or not for a particular game with the compare several machine learning algorithms to predict human behavior and health using the data collected from mobile devices and sensors placed on patients. In this study, we have obtained the dataset from a similar study done on mhealth. The dataset contains vital signs recordings of ten volunteers from different backgrounds. They had to perform several physical activities with a sensor placed on their bodies. Our study used 5 machine learning algorithms (XGBoost, Naive Bayes, Decision Tree, Random Forest, and Logistic Regression) to analyze and predict human health behavior. XGBoost performed better compared to the other machine learning algorithms and achieved 95.2% accuracy, 99.5% in sensitivity, 99.5% in specificity, and 99.66% in F1 score. Our research indicated a promising future in mhealth being used to predict human behavior and further research and exploration need to be done for it to be available for commercial use specifically in the sports industry.

翻译：移动电话及其他电子设备已在无需手动录入的情况下辅助数据收集。本文重点关注移动健康数据，这类数据通过移动设备实时采集临床健康信息并追踪患者生命体征。本研究旨在比较多种机器学习算法，为不同规模的运动队提供运动员是否适合特定比赛项目的决策依据，利用从移动设备及患者佩戴传感器采集的数据预测人体行为与健康状态。研究中，我们从一项类似的移动健康研究中获取数据集，包含十位不同背景志愿者的生命体征记录。志愿者在身体佩戴传感器的情况下完成多项体力活动。本研究采用五种机器学习算法（XGBoost、朴素贝叶斯、决策树、随机森林与逻辑回归）分析与预测人类健康行为。其中，XGBoost算法表现最优，达到95.2%的准确率、99.5%的灵敏度、99.5%的特异度及99.66%的F1分数。研究表明，移动健康在人体行为预测领域具有广阔前景，但需进一步研究与探索方可实现在体育行业等领域的商业应用。