STTR Phase I: Gait Tracker Shoe for long term accurate measurement of walking and running

这项小型企业技术转移（STTR）第一阶段项目的广泛影响/商业潜力集中在为日常现实世界活动提供准确步态数据的能力上。当然，这可能会改变运动康复领域的游戏规则，在这个领域，自我报告的依从性是一个问题。随着跑步活动的增加，这可能会改变跑步者（甚至是越野滑雪者或经过修改算法的滑冰者）的训练方式。有了这种能力，跑步者将通过训练或比赛了解他们的步态历史。对于一个需要长期监测的人来说，这项技术可以首次提供一个人在现实世界中如何移动的详细数据。用户不仅可以确定所走的步数和每一步的大小，还可以确定用户执行的活动的详细信息，例如上楼或下楼；上下斜坡，步行还是跑步等等。最后，imu可以被包装在不同的设备中，这些设备可以用来客观地测量除步行以外的康复活动。拟议的项目利用我们从惯性测量单元（IMUS）收集准确信息的能力，并将其与创新的鞋设计相结合，从而为个人提供经济的实验室外运动测量和报告。利用新的数学算法，来自IMU传感器的数据可以准确地确定步态，跑步或行走参数。它们还可以区分一个人从事的运动活动的类型，比如上山和在平地上跑步。长时间分析这些数据将是大数据（需要特殊分析工具的大数据集）使用的实际应用。这项工作将允许在康复环境中低成本报告，避免自我报告和监督康复的高成本问题。这项工作还将为智能手机开发应用程序，以便向个人提供实时反馈，以确保他们按照规定进行康复治疗。采用蓝牙数据传输和感应充电技术的先进制造技术将使imu能够放置在鞋底内，免受各种因素的影响。

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project centers around the ability to provide accurate gait data for every day real world activities. Certainly this could be something of a game changer in the field of locomotion rehabilitation where self-reporting of compliance is an issue. In the growing activity of running this can change the way runners (or even cross-country skiers or skaters with modified algorithms) train. With this capability runners would know the history of their gait through a training session or race. For an individual with a condition that needs long term monitoring this technology can provide for the first time, detailed data on how one locomotes in the real world. One can determine not only the number of steps one takes and how large each step was, but detailed information on what activities the user performed, such as walking up or down stairs; walking up or down slopes, walking versus running, etc. Finally, IMUs could be packaged in different devices that could be used to objectively measure rehabilitation activities other than walking.The proposed project uses advances in our ability to gather accurate information from Inertial Measurement Units (IMUS) and combines them with an innovative shoe design to allow economical measurement and reporting of out-of-lab locomotion for individuals. Using new mathematical algorithms the data from the IMU sensors can accurately determine gait, running or walking, parameters. They can also distinguish the type of locomotor activity a person is engaged in, such as walking up a hill vs running on a level surface. The analysis of this data over extended periods of time will be a practical application of the use of Big Data (large data sets where special analysis tools are needed). This work will allow low cost reporting in rehabilitation settings avoiding the issues of self-reporting and the high cost of supervised rehabilitation. This work will also develop apps for smartphones to allow real time feedback to individuals to ensure that they are following their prescribed rehabilitation. Advanced manufacturing with Bluetooth data transmission and induction charging will allow the IMUs to be placed inside the shoe sole protected from the elements.