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

这个小型企业技术转让(STTR)第一阶段项目的更广泛影响/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，自我报告合规性是一个问题。在日益增长的跑步活动中，这可能会改变跑步者(甚至是越野滑雪者或修改了算法的滑雪者)的训练方式。有了这种能力，跑步者就可以通过训练或比赛了解自己的步态历史。对于一个需要长期监测的人来说，这项技术可以第一次提供一个人在现实世界中如何运动的详细数据。人们不仅可以确定一个人走了多少步，每一步有多大，还可以确定用户进行了哪些活动的详细信息，例如走上或走下楼梯；走或下斜坡，走还是跑等等。最后，IMU可以封装在不同的设备中，可以用来客观地测量行走以外的康复活动。拟议中的项目利用我们从惯性测量单元(IMU)收集准确信息的能力的进步，并将其与创新的鞋子设计相结合，以便经济地测量和报告个人的走出实验室的运动。使用新的数学算法，来自IMU传感器的数据可以准确地确定步态、跑步或步行参数。他们还可以区分一个人正在进行的运动活动的类型，比如走上山坡和在平坦的地面上跑步。在较长时间内对这些数据进行分析将是使用大数据(需要特殊分析工具的大数据集)的实际应用。这项工作将使康复环境中的低成本报告成为可能，从而避免自我报告的问题和受监督的康复成本高昂的问题。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，以确保他们正在遵循规定的康复方案。具有蓝牙数据传输和感应充电的先进制造将允许将IMU放置在鞋底内，使其免受元素的影响。

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.