CAREER: Advances in Monitoring Human Performance: Moving Wearable Technology from the Expert to Nonexpert User

可穿戴计算技术正在迅速普及，并在我们的日常生活中发挥着越来越大的作用。 PI在这项研究中的重点是开发技术，用于在中风康复的背景下由非专家监测人类表现，这将适用于广泛的应用。 可以由非专家解释的强大的性能指标将使人们能够以目前不可能的方式跟踪他们的福祉。随着技术在家庭环境中的应用，有可能更好地让用户参与自我监控，增加动力，并改善与健康相关的活动的运动策略。 从医疗保健专业人士的角度来看，家庭中的可穿戴技术可以允许临床医生改变时间的平衡，以便强调教育和与患者一起完成任务，因为可穿戴技术将提供有关依从性历史和进展的信息。 来自传感器的纵向数据还将允许改进对患者特异性剂量反应灵敏度的评估。 这里实现的以人为中心的研究方法也将为系统建模理论提供新的见解，特别是如何形式化系统架构中人类和计算机实体之间的关系。由于该研究将涉及健康和中风参与者群体，项目成果将包括一个新的数据库，其中包含参与者人口统计数据、专家成果指标和日常家庭任务表现，这将允许新算法的进步，并将提供一种方法来比较不同人群的算法性能。PI认为，高保真度的运动传感是提高人类表现、目标监测、和幸福 为此，在这个项目中，她将通过动态系统建模和信号处理的进步来扩展可穿戴运动传感技术的能力，以解释个体运动和顺应性结构的潜在变化。 将为那些在传感器技术和生理系统方面知识有限的人（非专家）开发一个网络人平台，方法是分析性能指标和考虑到最终用户的决策界面。 这项工作将涉及三个相关的推力，将在中风康复的背景下证明：在自然环境中的相关任务的变异性的表征;应用估计算法和调查的性能指标鲁棒性的不确定性在自然环境中;和评估的决策界面协同预期的最终用户。 PI将实施新型估计和校准算法来为性能指标的生成提供信息，并将这些参数集成到用户界面中，该界面在人类研究中进行评估，作为跨专业知识水平决策的平台。 通过连接生物力学和控制理论，将为可穿戴运动传感设备提供新的功能，这些设备将相关的非线性模型与适当的随机性集成在一起，这反过来将为生物力学界带来令人兴奋的研究机会，以了解自然环境中的运动行为，以及从方法的角度来看控制理论的适应性和扩展，这是由于在保持符合要求的系统的校准方面存在新的挑战，潜在的可变性。

Wearable computing technology is rapidly proliferating and playing an increasing role in our daily lives. The PI's focus in this research is on developing technology for the monitoring by non-experts of human performance within the context of stroke rehabilitation, which will be applicable to a wide spectrum of applications. Robust performance metrics that can be interpreted by a non-expert would enable people to track their well-being in a manner not currently possible. With technology in the home environment, there is the potential to better engage the user in self-monitoring, to increase motivation, and to improve motion strategies for activities related to well-being. From the healthcare professional's perspective, wearable technology in the home could allow the clinician to change the balance of time so as to emphasize educating and working with the patient on enabling tasks, because the wearable technology would provide information on compliance history and progress. The longitudinal data from the sensors would also permit improved evaluation of patient-specific dose-response sensitivity. The human-centered research methodology implemented here will also provide new insights into systems modeling heuristics, in particular how to formalize relationships between the human and computer entities of the systems architecture. Because the research will involve both healthy and stroke participant groups, project outcomes will include a novel database with participant demographics, expert outcome measures, and daily home task performance which will permit the advancement of new algorithms and will provide a way to compare algorithm performance across populations.The PI argues that higher fidelity motion sensing is the key to empowering improved human performance, goal monitoring, and well-being. To this end, in this project she will extend the capabilities of wearable motion sensing technology through advances in dynamic system modeling and signal processing to account for the underlying variability in motion and compliant structure of the individual. A cyber-human platform will be developed for those with limited knowledge in sensor technology and physiological systems (non-experts), through analysis of performance metrics and decision-making interfaces with the end user in mind. The effort will involve three related thrusts that will be demonstrated within the context of stroke rehabilitation: characterization of variability for relevant tasks in a natural environment; application of estimation algorithms and investigation of performance metrics robust to uncertainties in the natural environment; and evaluation of decision-making interfaces synergistic with the expected end user. The PI will implement novel estimation and calibration algorithms to inform performance metric generation, and will integrate these parameters into a user interface that is evaluated in human studies as a platform for decision making across expertise level. By bridging biomechanics and control theory, new capabilities will be enabled for wearable motion-sensing devices that integrate relevant nonlinear models with the appropriate stochasticity, which in turn will lead to exciting research opportunities for the biomechanics community to understand motor behavior in natural settings, as well as adaptations and extensions in control theory from a methods perspective due to new challenges in maintaining calibrations for systems with compliance and underlying variability.