Development of a Novel Integrative Recovery Index Using Machine Learning

体育运动的成功通常需要精心设计的训练计划，平衡训练强度/量和充分的休息/恢复，以允许渐进的身体和心理适应。制定训练计划的挑战是理解和评估训练负荷需要衰减的点。这降低了进入被称为“功能过度”的持续和不受欢迎的疲劳状态的风险。一个全面的指数，补充训练计划，以最大限度地提高训练负荷，而不损害恢复目前还不存在。然而，当代可穿戴智能手表技术显示出开发这种参数的希望。 与目前市面上的其他智能手表不同，我们的工业合作伙伴Health Gauge开发的Phoenix智能手表能够捕捉血压指标，并整合与情绪状态相关的心理定性反应选项。该项目的目标是与Health Gauge合作，将Phoenix智能手表的独特功能与机器学习（ML）相结合，开发一种新颖的、优化的训练恢复指数。该项目的第一阶段将涉及校准凤凰手表，以实验室为基础的生物特征测量，包括心率，血压，心率变异性，温度和血氧饱和度在休息，动态和长期（走动）条件下的大学运动员样本。第二阶段将涉及使用Phoenix从受过训练的自行车手那里获得生理和心理变量，以开发恢复指数，随后使用ML在手表中构建嵌入框架，该框架将检测状态并使用扩展的短期记忆模型将状态识别与训练输入相结合。一旦模型完全开发，其预测“功能过度状态”的能力可以在其他人群中得到验证。Health Gauge将把这一指数纳入其应用程序，使加拿大人能够使用。除了监测运动员的准备情况外，我们还看到该恢复指数有可能指导紧急医疗人员，医疗保健工作者，执法人员和军事人员的重返工作岗位政策和建议。

Success in athletics usually requires well-designed training programs that balance training intensity/volume and adequate rest/recovery to allow for progressive physical and psychological adaptation. The challenge with developing training programs is to understand and assess the point at which training loads require attenuation. This reduces the risk of entering into a persistent and unwelcome fatigued state referred to as 'functional overreaching'. A comprehensive index that complements training programs to maximize training loads without compromising recovery currently does not exist. However, contemporary wearable smartwatch technology shows promise to develop such a parameter. Unlike other smartwatches currently available, the Phoenix smartwatch developed by our industrial partner Health Gauge is capable of capturing blood pressure metrics and integrating psychological qualitative response options related to mood states. The goal of this project is to work with Health Gauge to combine the unique capabilities of the Phoenix smartwatch with machine learning (ML) to develop a novel, optimized, training recovery index'. Phase 1 of the project will involve calibrating the Phoenix watch to lab-based biometric measures including heart rate, blood pressure, heart rate variability, temperature and blood oxygen saturation under resting, dynamic and prolonged (ambulatory) conditions in a sample of university athletes. Phase 2 will involve obtaining physiological and psychological variables from trained cyclists using the Phoenix to develop the recovery index with subsequent use of ML to build an embedding framework into the watch that will detect states and combine state recognition with training inputs using an extended short-term memory model. Once the model is fully developed, its ability to predict a 'functionally overreached state' can be validated in other populations. Health Gauge will incorporate this index into their application, making it accessible to Canadians. In addition to monitoring the readiness of athletes, we also see the potential for this recovery index to guide return-to-work policies and recommendations for emergency medical personnel, healthcare workers, law enforcement, and military personnel.