CPS: Medium: Collaborative Research: Monitoring Human Performance with Wearable Accelerometers

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。本研究的目的是开发一个由加速度计和新型机器学习算法组成的网络物理系统，以分析一系列驱动医疗保健应用中的数据。该方法是开发新的机器学习算法，用于时间分割，分类和检测人体运动的微妙元素。这些技术将允许使用轻量级可穿戴系统对人体运动进行量化，并改进对医疗状况的全天候监测和评估。这项研究的科学贡献在于推进机器学习和人类感知，通过以下方式支持改进医疗诊断和治疗监测：(i)通过传感器数据分析对人类活动和症状进行建模；（ii）整合和融合来自多个加速度计的信息进行实时监测；（iii）通过在诊断评估中应用最新技术的评估来验证自动检测的有效性。（iv）开发新的机器学习方法，用于时间分割、分类和发现多种时间模式，以区分时间信号；(v)提供高质量的措施来表征细微的人类运动。这些算法将推动机器学习在无监督和半监督学习领域的发展。该研究的主要应用领域包括认知障碍患者的工作指导、膝骨关节炎的远程康复、作为老年人健康指标的平衡和步态变异性评估以及帕金森病患者的评估方法。这项研究是高度跨学科的，将培养研究生从事卫生和监测系统技术创新的职业。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The objective of this research is to develop a cyber-physical system composed of accelerometers and novel machine learning algorithms to analyze data in the context of a set of driving health care applications. The approach is to develop novel machine learning algorithms for temporal segmentation, classification, and detection of subtle elements of human motion. These techniques will allow quantification of human motion and improved full-time monitoring and assessment of medical conditions using a lightweight wearable system. The scientific contribution of this research is in advancing machine learning and human sensing in support of improved medical diagnoses and treatment monitoring by (i) modeling human activity and symptoms through sensor data analysis, (ii) integrating and fusing information from several accelerometers to monitor in real-time, (iii) validating the efficacy of the automated detection through assessments applying the state of the art in diagnostic evaluation, (iv) developing novel machine learning methods for temporal segmentation, classification, and discovery of multiple temporal patterns that discriminate between temporal signals, and (v) providing quality measures to characterize subtle human motion. These algorithms will advance machine learning in the area of unsupervised and semisupervised learning. The driving applications for this research are job coaching for people with cognitive disabilities, tele-rehabilitation for knee osteo-arthritis, assessing variability in balance and gait as an indicator of health of older adults, and measures for assessing Parkinson's patients. This research is highly interdisciplinary and will train graduate students for careers in developing technological innovations in health and monitoring systems.