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

职业：监测人类表现的进展：将可穿戴技术从专家转向非专家用户

• 批准号: 1453141
• 负责人: Leia Stirling
• 金额: $ 62.59万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1453141&HistoricalAwards=false
• 关键词: CAREER; Advances; Monitoring; Human; Performance

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.

可穿戴计算技术正在迅速扩散，并在我们的日常生活中发挥越来越大的作用。PI在这项研究中的重点是开发技术，由非专家在中风康复的背景下监测人类表现，这将适用于广泛的应用。可以由非专家解释的稳健的绩效指标将使人们能够以一种目前不可能的方式跟踪他们的幸福感。有了家庭环境中的技术，就有可能更好地让用户参与自我监控，增加动力，并改进与幸福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平衡，从而强调在启用任务方面教育患者和与患者合作，因为可穿戴技术将提供有关合规历史和进展的信息。来自传感器的纵向数据也将允许改进对患者特定剂量-反应敏感性的评估。这里实现的以人为中心的研究方法还将为系统建模启发式提供新的见解，特别是如何将系统体系结构的人和计算机实体之间的关系形式化。由于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，其中包括参与者人口统计、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，更高保真度的运动感知是增强人类表现、目标监控和幸福感的关键。为此，在这个项目中，她将通过动态系统建模和信号处理方面的进步来扩展可穿戴运动传感技术的能力，以考虑到个人运动和顺应结构的潜在可变性。将通过分析性能指标和考虑到最终用户的决策界面，为在传感器技术和生理系统(非专家)方面知识有限的人开发一个网络人平台。这项工作将涉及三个相关的推力，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；应用评估算法和调查对自然环境中不确定因素的稳健性能衡量标准；以及评估与预期最终用户的决策界面的协同作用。PI将实施新的估计和校准算法以通知性能指标生成，并将这些参数集成到用户界面中，该用户界面将在人体研究中作为跨专业水平决策的平台进行评估。通过将生物力学和控制理论联系起来，可穿戴式运动传感设备将能够实现将相关的非线性模型与适当的随机性相结合的新功能，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，并从方法的角度对控制理论进行适应和扩展，因为在维护具有合规性和潜在变异性的系统的校准方面存在新的挑战。