论文标题
体育锻炼的建议和成功预测，使用相互连接的复发神经网络
Physical Exercise Recommendation and Success Prediction Using Interconnected Recurrent Neural Networks
论文作者
论文摘要
不健康的行为，例如身体不活动和不健康的食物选择，是发达国家的主要医疗费用驱动因素。由智能手机和智能手表提供的普遍计算，感应和通信技术使人们有可能在日常生活中支持个人，以发展更健康的生活方式。在本文中，我们提出了一个锻炼建议系统，该系统也可以预测个人成功率。该系统由两个相互连接的复发性神经网络（RNN）组成，使用锻炼史来推荐每个人的下一个锻炼活动。然后，该系统预测了个人成功完成预测活动的可能性。该相互联系的RNN模型的预测准确性是对从四周移动健康实验中发表的数据进行评估的，并显示可改善计算认知模型的先前预测。
Unhealthy behaviors, e.g., physical inactivity and unhealthful food choice, are the primary healthcare cost drivers in developed countries. Pervasive computational, sensing, and communication technology provided by smartphones and smartwatches have made it possible to support individuals in their everyday lives to develop healthier lifestyles. In this paper, we propose an exercise recommendation system that also predicts individual success rates. The system, consisting of two inter-connected recurrent neural networks (RNNs), uses the history of workouts to recommend the next workout activity for each individual. The system then predicts the probability of successful completion of the predicted activity by the individual. The prediction accuracy of this interconnected-RNN model is assessed on previously published data from a four-week mobile health experiment and is shown to improve upon previous predictions from a computational cognitive model.
