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发展，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在中风康复背景下由非专家监测人类表现的技术，这将适用于广泛的应用。非专家也能解释的稳健的绩效指标将使人们能够以目前不可能的方式跟踪自己的健康状况。随着家庭环境中的技术，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。从医疗保健专业人员的角度来看，家庭中的可穿戴技术可以让临床医生改变时间的平衡，从而强调教育和与患者一起完成使能任务，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也将允许改进对患者特异性剂量反应敏感性的评估。这里实现的以人为中心的研究方法也将为系统建模启发式提供新的见解，特别是如何形式化系统体系结构的人和计算机实体之间的关系。因为这项研究将涉及健康和中风参与者群体，项目结果将包括一个新的数据库，其中包含参与者人口统计数据、专家结果测量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，更高保真度的运动传感是提高人类表现、目标监控和福祉的关键。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，以解释个人运动和顺应结构的潜在可变性。通过分析性能指标和最终用户的决策界面，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-人类平台。这项工作将涉及三个相关的重点，将在中风康复的背景下进行演示：表征自然环境中相关任务的可变性；估计算法的应用及对自然环境不确定性鲁棒性能指标的研究并评价决策界面与预期最终用户的协同性。PI将实施新的估计和校准算法，为性能指标生成提供信息，并将这些参数集成到用户界面中，作为跨专业水平决策的平台，在人类研究中进行评估。通过连接生物力学和控制理论，可穿戴式运动传感设备的新功能将成为可能，这些设备将相关的非线性模型与适当的随机性相结合，这反过来将为生物力学社区带来令人兴奋的研究机会，以了解自然环境中的运动行为。以及从方法角度对控制理论的适应和扩展，因为在维护具有顺应性和潜在可变性的系统的校准方面存在新的挑战。

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.