A Software Platform for Sensor-based Movement Disorder Recognition

 描述(申请人提供)：此第二阶段的目标是通过开发一种基于自动传感器的方法来跟踪日常生活中无脚本活动中广泛运动障碍的存在和严重程度，从而增强高级大脑和行为研究工具的可用性[PA-14-250]。来自穿戴在身上的传感器不断更新和解释的信息将提供准确、客观和高分辨率(1 S)。帕金森氏病的震颤、运动障碍、运动迟缓、冰冻和步态障碍以及特发性震颤的姿势/运动性震颤的运动症状严重程度的测量。它将使研究人员能够评估运动障碍往往复杂和动态的性质，目前的自我报告和纸笔工具的标准很难捕捉到这一点。可穿戴传感器技术的进步促进了这样的解决方案，但目前还没有运动障碍识别设备能够有效地解释来自非脚本活动的传感器数据，供美国超过4500万运动障碍患者使用。我们的方法的独特之处在于，我们正在开发一个通用应用生成器(AG)软件平台，其中包含信号处理模块，可以轻松配置为提供不同疾病的自动识别，而不需要从头开始为每个疾病准备单独的算法。第一阶段通过开发一个基本的AG平台建立了概念验证，该平台通过新型混合传感器实现了对帕金森病(PD)患者的震颤、运动障碍和步态冻结的自动识别，该传感器通过表面肌电(SEMG)和加速计记录提供肌肉活动和运动数据。第二阶段将继续发展，包括更广泛的帕金森病运动障碍，以及其他神经疾病。AIM 1将通过结合表面肌电信号和惯性测量单元(IMU)传感器来创建增强的AG平台，以更完整地描述非自愿运动，并降低跟踪其他疾病时的风险。人体受试者测试将提供一个传感器数据库，用于测试IMU传感器的准确性，并将软组织伪影降至最低。将使用增强型平台更新第一阶段识别算法。AIM 2将利用增强的平台开发新的识别应用程序，跟踪帕金森病患者的运动迟缓和步态障碍，以及特发性震颤患者的姿势和运动性震颤。我们的目标是使用独立于用户的算法，在不受限制的监控条件下实现5%的错误率。AIM 3将提供一款便携式商用前设备，配备必要的硬件、软件、用户界面和报告生成器，以有效地监测PD、特发性震颤和坐/站/走活动。该系统将使用平板电脑收集和处理sEMG/IMU数据，以增强可用性。运动障碍专家和潜在的最终用户将指导第二阶段的开发，并协助我们制定其他神经系统疾病(如脑瘫、肌张力障碍、肌萎缩侧索硬化症和不宁腿综合征)的未来商业化计划。它也将构成患者可手术的基础 临床使用的设备。

 DESCRIPTION (provided by applicant): The goal of this Phase II is to enhance the availability of advanced brain and behavior research tools [PA-14-250] by developing an automated sensor-based means of tracking the presence and severity of a broad spectrum of movement disorders during unscripted activities of daily living. The continuously updated and interpreted information from body-worn sensors will provide accurate, objective, and high resolution (1 s.) measurement of motor symptom severity of tremor, dyskinesia, bradykinesia, freezing and gait disorders in Parkinson's disease and postural/kinetic tremor in essential tremor. It will allow researchers to assess the oftentimes complex and dynamic nature of movement disorders, which is poorly captured by the current standard of self-reports and pencil-and-paper instruments. Advances in wearable sensor technology have facilitated such a solution, but there are currently no movement disorder recognition devices capable of interpreting sensor data from non- scripted activity in an effective manner for the more than 45 million people in the U.S. with movement disorders. Our approach is unique in that we are developing a generic Application Generator (AG) software platform containing signal processing modules that can be readily configured to provide automated recognition for different disorders without the need to prepare separate algorithms from scratch for each. Phase I established a proof of concept by developing a rudimentary AG platform that achieved automatic recognition of tremor, dyskinesia and freezing-of-gait in patients with Parkinson's disease (PD) from novel hybrid sensors that provided both muscle activity and movement data through surface electromyographic (sEMG) and accelerometer recordings. Phase II will continue the development to include a broader range of PD movement disorders, as well as other neurological conditions. Aim 1 will create an enhanced AG Platform by incorporating combined sEMG and inertial measurement unit (IMU) sensors to more completely describe involuntary movements and reduce the risk when tracking additional disorders. Human subject testing will provide a sensor database for testing IMU sensor accuracy and minimizing soft tissue artifacts. The Phase I recognition algorithms will be updated using the enhanced platform. Aim 2 will use the enhanced platform to develop new recognition applications that track bradykinesia and gait disorders in PD, and postural and kinetic tremors in patients with essential tremor. Our goal is to achieve error rates < 5% during unconstrained monitoring conditions with user- independent algorithms. Aim 3 will deliver a portable pre-commercial device with the requisite hardware, software, user interface, and report generator to effectively monitor PD, essential tremor, and sitting/standing/walking activity. The system will collect and process sEMG/IMU data using a tablet PC to enhance usability. Movement disorder experts and prospective end-users will guide the Phase II development and assist us with future commercialization plans for other neurological conditions such as cerebral palsy, dystonia, ALS, and restless leg syndrome. It will also form the basis for a patient-operable device for clinical use.